{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27500e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.1\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee948974",
   "metadata": {},
   "source": [
    "The three extensions below are optional, for more information, see\n",
    "- `watermark`:  https://github.com/rasbt/watermark\n",
    "- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\n",
    "- `nb_black`: https://github.com/dnanhkhoa/nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7aadc729",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pycodestyle_magic\n",
    "%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a488475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916685d3",
   "metadata": {},
   "source": [
    "<a href=\"https://pytorch.org\"><img src=\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\" width=\"90\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\"https://www.pytorchlightning.ai\"><img src=\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\" width=\"150\"/></a>\n",
    "\n",
    "# DenseNet121 Trained on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb94f20",
   "metadata": {},
   "source": [
    "The network in this notebook is an implementation of the DenseNet-121 [1] architecture on the CIFAR-10[2] dataset.  \n",
    "\n",
    "The following figure illustrates the main concept of DenseNet: within each \"dense\" block, each layer is connected with each previous layer -- the feature maps are concatenated.\n",
    "\n",
    "\n",
    "![](../../pytorch_ipynb/images/densenet/densenet-fig-2.jpg)\n",
    "\n",
    "Note that this is somewhat related yet very different to ResNets. ResNets have skip connections approx. between every other layer (but don't connect all layers with each other). Also, ResNets skip connections work via addition\n",
    "\n",
    "$$\\mathbf{x}_{\\ell}=H_{\\ell}\\left(\\mathbf{X}_{\\ell-1}\\right)+\\mathbf{X}_{\\ell-1},$$\n",
    "\n",
    "whereas $H_{\\ell}(\\cdot)$ can be a composite function  of operations such as Batch Normalization (BN), rectified linear units (ReLU), Pooling, or Convolution (Conv).\n",
    "\n",
    "In DenseNets, all the previous feature maps $\\mathbf{X}_{0}, \\dots, \\mathbf{X}_{\\ell}-1$ of a feature map $\\mathbf{X}_{\\ell}$ are concatenated:\n",
    "\n",
    "$$\\mathbf{x}_{\\ell}=H_{\\ell}\\left(\\left[\\mathbf{x}_{0}, \\mathbf{x}_{1}, \\ldots, \\mathbf{x}_{\\ell-1}\\right]\\right).$$\n",
    "\n",
    "Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\n",
    "\n",
    "\n",
    "\n",
    "![](../../pytorch_ipynb/images/densenet/densenet-tab-1-dnet121.jpg)\n",
    "\n",
    "\n",
    "### References\n",
    "\n",
    "- [1] Densely Connected Convolutional Networks, https://arxiv.org/abs/1608.06993\n",
    "- [2] https://en.wikipedia.org/wiki/CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdf49f9",
   "metadata": {},
   "source": [
    "## General settings and hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b24bb89",
   "metadata": {},
   "source": [
    "- Here, we specify some general hyperparameter values and general settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed17d19c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 20\\nLEARNING_RATE = 0.005\\nNUM_WORKERS = 4\";\n",
       "                var nbb_formatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 20\\nLEARNING_RATE = 0.005\\nNUM_WORKERS = 4\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCH_SIZE = 256\n",
    "NUM_EPOCHS = 20\n",
    "LEARNING_RATE = 0.005\n",
    "NUM_WORKERS = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579ff844",
   "metadata": {},
   "source": [
    "- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814d3fac",
   "metadata": {},
   "source": [
    "## Implementing a Neural Network using PyTorch Lightning's `LightningModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93962a9b",
   "metadata": {},
   "source": [
    "- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\n",
    "- In essence, `LightningModule` is a wrapper around a PyTorch module.\n",
    "- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c514273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"# The following code cell that implements the DenseNet-121 architecture \\n# is a derivative of the code provided at \\n# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\n\\nimport re\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\nimport torch.utils.checkpoint as cp\\nfrom collections import OrderedDict\\n\\n\\ndef _bn_function_factory(norm, relu, conv):\\n    def bn_function(*inputs):\\n        concated_features = torch.cat(inputs, 1)\\n        bottleneck_output = conv(relu(norm(concated_features)))\\n        return bottleneck_output\\n\\n    return bn_function\\n\\n\\nclass _DenseLayer(nn.Sequential):\\n    def __init__(self, num_input_features, growth_rate,\\n                 bn_size, drop_rate, memory_efficient=False):\\n        super(_DenseLayer, self).__init__()\\n        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\\n        self.add_module('relu1', nn.ReLU(inplace=True)),\\n        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\\n                                           growth_rate, kernel_size=1, stride=1,\\n                                           bias=False)),\\n        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\\n        self.add_module('relu2', nn.ReLU(inplace=True)),\\n        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\\n                                           kernel_size=3, stride=1, padding=1,\\n                                           bias=False)),\\n        self.drop_rate = drop_rate\\n        self.memory_efficient = memory_efficient\\n\\n    def forward(self, *prev_features):\\n        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\n        if self.memory_efficient and any(\\n                prev_feature.requires_grad for \\n                prev_feature in prev_features):\\n            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\n        else:\\n            bottleneck_output = bn_function(*prev_features)\\n        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\n        if self.drop_rate > 0:\\n            new_features = F.dropout(new_features, p=self.drop_rate,\\n                                     training=self.training)\\n        return new_features\\n\\n\\nclass _DenseBlock(nn.Module):\\n    def __init__(self, num_layers, num_input_features, \\n                 bn_size, growth_rate, drop_rate, memory_efficient=False):\\n        super(_DenseBlock, self).__init__()\\n        for i in range(num_layers):\\n            layer = _DenseLayer(\\n                num_input_features + i * growth_rate,\\n                growth_rate=growth_rate,\\n                bn_size=bn_size,\\n                drop_rate=drop_rate,\\n                memory_efficient=memory_efficient,\\n            )\\n            self.add_module('denselayer%d' % (i + 1), layer)\\n\\n    def forward(self, init_features):\\n        features = [init_features]\\n        for name, layer in self.named_children():\\n            new_features = layer(*features)\\n            features.append(new_features)\\n        return torch.cat(features, 1)\\n\\n\\nclass _Transition(nn.Sequential):\\n    def __init__(self, num_input_features, num_output_features):\\n        super(_Transition, self).__init__()\\n        self.add_module('norm', nn.BatchNorm2d(num_input_features))\\n        self.add_module('relu', nn.ReLU(inplace=True))\\n        self.add_module('conv', nn.Conv2d(\\n            num_input_features, num_output_features,\\n            kernel_size=1, stride=1, bias=False))\\n        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\\n\\n\\nclass PyTorchModel(nn.Module):\\n    r\\\"\\\"\\\"Densenet-BC model class, based on\\n    `\\\"Densely Connected Convolutional Networks\\\"\\n    <https://arxiv.org/pdf/1608.06993.pdf>`_\\n\\n    Args:\\n        growth_rate (int) - how many filters to add each layer (`k` in paper)\\n        block_config (list of 4 ints) - how many layers in each pooling block\\n        num_init_featuremaps (int) - the number of filters to learn in the \\n        first convolution layer bn_size (int) - multiplicative factor for \\n        number of bottle neck layers (i.e. bn_size * k features \\n        in the bottleneck layer) drop_rate (float) - dropout rate after \\n        each dense layer num_classes (int) - number of classification classes\\n        memory_efficient (bool) - If True, uses checkpointing. \\n        Much more memory efficient, but slower. Default: *False*. \\n        See `\\\"paper\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\n    \\\"\\\"\\\"\\n\\n    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\\n                 num_init_featuremaps=64, bn_size=4, drop_rate=0, \\n                 num_classes=1000, memory_efficient=False,\\n                 grayscale=False):\\n\\n        super().__init__()\\n\\n        # First convolution\\n        if grayscale:\\n            in_channels = 1\\n        else:\\n            in_channels = 3\\n        \\n        self.features = nn.Sequential(OrderedDict([\\n            ('conv0', nn.Conv2d(\\n                in_channels=in_channels,\\n                out_channels=num_init_featuremaps,\\n                kernel_size=7,\\n                stride=2,\\n                padding=3,\\n                bias=False)),  # bias is redundant when using batchnorm\\n            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\\n            ('relu0', nn.ReLU(inplace=True)),\\n            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\n        ]))\\n\\n        # Each denseblock\\n        num_features = num_init_featuremaps\\n        for i, num_layers in enumerate(block_config):\\n            block = _DenseBlock(\\n                num_layers=num_layers,\\n                num_input_features=num_features,\\n                bn_size=bn_size,\\n                growth_rate=growth_rate,\\n                drop_rate=drop_rate,\\n                memory_efficient=memory_efficient\\n            )\\n            self.features.add_module('denseblock%d' % (i + 1), block)\\n            num_features = num_features + num_layers * growth_rate\\n            if i != len(block_config) - 1:\\n                trans = _Transition(num_input_features=num_features,\\n                                    num_output_features=num_features // 2)\\n                self.features.add_module('transition%d' % (i + 1), trans)\\n                num_features = num_features // 2\\n\\n        # Final batch norm\\n        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\\n\\n        # Linear layer\\n        self.classifier = nn.Linear(num_features, num_classes)\\n\\n        # Official init from torch repo.\\n        for m in self.modules():\\n            if isinstance(m, nn.Conv2d):\\n                nn.init.kaiming_normal_(m.weight)\\n            elif isinstance(m, nn.BatchNorm2d):\\n                nn.init.constant_(m.weight, 1)\\n                nn.init.constant_(m.bias, 0)\\n            elif isinstance(m, nn.Linear):\\n                nn.init.constant_(m.bias, 0)\\n\\n    def forward(self, x):\\n        features = self.features(x)\\n        out = F.relu(features, inplace=True)\\n        out = F.adaptive_avg_pool2d(out, (1, 1))\\n        out = torch.flatten(out, 1)\\n        logits = self.classifier(out)\\n        return logits\";\n",
       "                var nbb_formatted_code = \"# The following code cell that implements the DenseNet-121 architecture\\n# is a derivative of the code provided at\\n# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\n\\nimport re\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\nimport torch.utils.checkpoint as cp\\nfrom collections import OrderedDict\\n\\n\\ndef _bn_function_factory(norm, relu, conv):\\n    def bn_function(*inputs):\\n        concated_features = torch.cat(inputs, 1)\\n        bottleneck_output = conv(relu(norm(concated_features)))\\n        return bottleneck_output\\n\\n    return bn_function\\n\\n\\nclass _DenseLayer(nn.Sequential):\\n    def __init__(\\n        self,\\n        num_input_features,\\n        growth_rate,\\n        bn_size,\\n        drop_rate,\\n        memory_efficient=False,\\n    ):\\n        super(_DenseLayer, self).__init__()\\n        self.add_module(\\\"norm1\\\", nn.BatchNorm2d(num_input_features)),\\n        self.add_module(\\\"relu1\\\", nn.ReLU(inplace=True)),\\n        self.add_module(\\n            \\\"conv1\\\",\\n            nn.Conv2d(\\n                num_input_features,\\n                bn_size * growth_rate,\\n                kernel_size=1,\\n                stride=1,\\n                bias=False,\\n            ),\\n        ),\\n        self.add_module(\\\"norm2\\\", nn.BatchNorm2d(bn_size * growth_rate)),\\n        self.add_module(\\\"relu2\\\", nn.ReLU(inplace=True)),\\n        self.add_module(\\n            \\\"conv2\\\",\\n            nn.Conv2d(\\n                bn_size * growth_rate,\\n                growth_rate,\\n                kernel_size=3,\\n                stride=1,\\n                padding=1,\\n                bias=False,\\n            ),\\n        ),\\n        self.drop_rate = drop_rate\\n        self.memory_efficient = memory_efficient\\n\\n    def forward(self, *prev_features):\\n        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\n        if self.memory_efficient and any(\\n            prev_feature.requires_grad for prev_feature in prev_features\\n        ):\\n            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\n        else:\\n            bottleneck_output = bn_function(*prev_features)\\n        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\n        if self.drop_rate > 0:\\n            new_features = F.dropout(\\n                new_features, p=self.drop_rate, training=self.training\\n            )\\n        return new_features\\n\\n\\nclass _DenseBlock(nn.Module):\\n    def __init__(\\n        self,\\n        num_layers,\\n        num_input_features,\\n        bn_size,\\n        growth_rate,\\n        drop_rate,\\n        memory_efficient=False,\\n    ):\\n        super(_DenseBlock, self).__init__()\\n        for i in range(num_layers):\\n            layer = _DenseLayer(\\n                num_input_features + i * growth_rate,\\n                growth_rate=growth_rate,\\n                bn_size=bn_size,\\n                drop_rate=drop_rate,\\n                memory_efficient=memory_efficient,\\n            )\\n            self.add_module(\\\"denselayer%d\\\" % (i + 1), layer)\\n\\n    def forward(self, init_features):\\n        features = [init_features]\\n        for name, layer in self.named_children():\\n            new_features = layer(*features)\\n            features.append(new_features)\\n        return torch.cat(features, 1)\\n\\n\\nclass _Transition(nn.Sequential):\\n    def __init__(self, num_input_features, num_output_features):\\n        super(_Transition, self).__init__()\\n        self.add_module(\\\"norm\\\", nn.BatchNorm2d(num_input_features))\\n        self.add_module(\\\"relu\\\", nn.ReLU(inplace=True))\\n        self.add_module(\\n            \\\"conv\\\",\\n            nn.Conv2d(\\n                num_input_features,\\n                num_output_features,\\n                kernel_size=1,\\n                stride=1,\\n                bias=False,\\n            ),\\n        )\\n        self.add_module(\\\"pool\\\", nn.AvgPool2d(kernel_size=2, stride=2))\\n\\n\\nclass PyTorchModel(nn.Module):\\n    r\\\"\\\"\\\"Densenet-BC model class, based on\\n    `\\\"Densely Connected Convolutional Networks\\\"\\n    <https://arxiv.org/pdf/1608.06993.pdf>`_\\n\\n    Args:\\n        growth_rate (int) - how many filters to add each layer (`k` in paper)\\n        block_config (list of 4 ints) - how many layers in each pooling block\\n        num_init_featuremaps (int) - the number of filters to learn in the\\n        first convolution layer bn_size (int) - multiplicative factor for\\n        number of bottle neck layers (i.e. bn_size * k features\\n        in the bottleneck layer) drop_rate (float) - dropout rate after\\n        each dense layer num_classes (int) - number of classification classes\\n        memory_efficient (bool) - If True, uses checkpointing.\\n        Much more memory efficient, but slower. Default: *False*.\\n        See `\\\"paper\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\n    \\\"\\\"\\\"\\n\\n    def __init__(\\n        self,\\n        growth_rate=32,\\n        block_config=(6, 12, 24, 16),\\n        num_init_featuremaps=64,\\n        bn_size=4,\\n        drop_rate=0,\\n        num_classes=1000,\\n        memory_efficient=False,\\n        grayscale=False,\\n    ):\\n\\n        super().__init__()\\n\\n        # First convolution\\n        if grayscale:\\n            in_channels = 1\\n        else:\\n            in_channels = 3\\n\\n        self.features = nn.Sequential(\\n            OrderedDict(\\n                [\\n                    (\\n                        \\\"conv0\\\",\\n                        nn.Conv2d(\\n                            in_channels=in_channels,\\n                            out_channels=num_init_featuremaps,\\n                            kernel_size=7,\\n                            stride=2,\\n                            padding=3,\\n                            bias=False,\\n                        ),\\n                    ),  # bias is redundant when using batchnorm\\n                    (\\\"norm0\\\", nn.BatchNorm2d(num_features=num_init_featuremaps)),\\n                    (\\\"relu0\\\", nn.ReLU(inplace=True)),\\n                    (\\\"pool0\\\", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\n                ]\\n            )\\n        )\\n\\n        # Each denseblock\\n        num_features = num_init_featuremaps\\n        for i, num_layers in enumerate(block_config):\\n            block = _DenseBlock(\\n                num_layers=num_layers,\\n                num_input_features=num_features,\\n                bn_size=bn_size,\\n                growth_rate=growth_rate,\\n                drop_rate=drop_rate,\\n                memory_efficient=memory_efficient,\\n            )\\n            self.features.add_module(\\\"denseblock%d\\\" % (i + 1), block)\\n            num_features = num_features + num_layers * growth_rate\\n            if i != len(block_config) - 1:\\n                trans = _Transition(\\n                    num_input_features=num_features,\\n                    num_output_features=num_features // 2,\\n                )\\n                self.features.add_module(\\\"transition%d\\\" % (i + 1), trans)\\n                num_features = num_features // 2\\n\\n        # Final batch norm\\n        self.features.add_module(\\\"norm5\\\", nn.BatchNorm2d(num_features))\\n\\n        # Linear layer\\n        self.classifier = nn.Linear(num_features, num_classes)\\n\\n        # Official init from torch repo.\\n        for m in self.modules():\\n            if isinstance(m, nn.Conv2d):\\n                nn.init.kaiming_normal_(m.weight)\\n            elif isinstance(m, nn.BatchNorm2d):\\n                nn.init.constant_(m.weight, 1)\\n                nn.init.constant_(m.bias, 0)\\n            elif isinstance(m, nn.Linear):\\n                nn.init.constant_(m.bias, 0)\\n\\n    def forward(self, x):\\n        features = self.features(x)\\n        out = F.relu(features, inplace=True)\\n        out = F.adaptive_avg_pool2d(out, (1, 1))\\n        out = torch.flatten(out, 1)\\n        logits = self.classifier(out)\\n        return logits\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# The following code cell that implements the DenseNet-121 architecture \n",
    "# is a derivative of the code provided at \n",
    "# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\n",
    "\n",
    "import re\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.checkpoint as cp\n",
    "from collections import OrderedDict\n",
    "\n",
    "\n",
    "def _bn_function_factory(norm, relu, conv):\n",
    "    def bn_function(*inputs):\n",
    "        concated_features = torch.cat(inputs, 1)\n",
    "        bottleneck_output = conv(relu(norm(concated_features)))\n",
    "        return bottleneck_output\n",
    "\n",
    "    return bn_function\n",
    "\n",
    "\n",
    "class _DenseLayer(nn.Sequential):\n",
    "    def __init__(self, num_input_features, growth_rate,\n",
    "                 bn_size, drop_rate, memory_efficient=False):\n",
    "        super(_DenseLayer, self).__init__()\n",
    "        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\n",
    "        self.add_module('relu1', nn.ReLU(inplace=True)),\n",
    "        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\n",
    "                                           growth_rate, kernel_size=1, stride=1,\n",
    "                                           bias=False)),\n",
    "        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\n",
    "        self.add_module('relu2', nn.ReLU(inplace=True)),\n",
    "        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\n",
    "                                           kernel_size=3, stride=1, padding=1,\n",
    "                                           bias=False)),\n",
    "        self.drop_rate = drop_rate\n",
    "        self.memory_efficient = memory_efficient\n",
    "\n",
    "    def forward(self, *prev_features):\n",
    "        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\n",
    "        if self.memory_efficient and any(\n",
    "                prev_feature.requires_grad for \n",
    "                prev_feature in prev_features):\n",
    "            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\n",
    "        else:\n",
    "            bottleneck_output = bn_function(*prev_features)\n",
    "        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\n",
    "        if self.drop_rate > 0:\n",
    "            new_features = F.dropout(new_features, p=self.drop_rate,\n",
    "                                     training=self.training)\n",
    "        return new_features\n",
    "\n",
    "\n",
    "class _DenseBlock(nn.Module):\n",
    "    def __init__(self, num_layers, num_input_features, \n",
    "                 bn_size, growth_rate, drop_rate, memory_efficient=False):\n",
    "        super(_DenseBlock, self).__init__()\n",
    "        for i in range(num_layers):\n",
    "            layer = _DenseLayer(\n",
    "                num_input_features + i * growth_rate,\n",
    "                growth_rate=growth_rate,\n",
    "                bn_size=bn_size,\n",
    "                drop_rate=drop_rate,\n",
    "                memory_efficient=memory_efficient,\n",
    "            )\n",
    "            self.add_module('denselayer%d' % (i + 1), layer)\n",
    "\n",
    "    def forward(self, init_features):\n",
    "        features = [init_features]\n",
    "        for name, layer in self.named_children():\n",
    "            new_features = layer(*features)\n",
    "            features.append(new_features)\n",
    "        return torch.cat(features, 1)\n",
    "\n",
    "\n",
    "class _Transition(nn.Sequential):\n",
    "    def __init__(self, num_input_features, num_output_features):\n",
    "        super(_Transition, self).__init__()\n",
    "        self.add_module('norm', nn.BatchNorm2d(num_input_features))\n",
    "        self.add_module('relu', nn.ReLU(inplace=True))\n",
    "        self.add_module('conv', nn.Conv2d(\n",
    "            num_input_features, num_output_features,\n",
    "            kernel_size=1, stride=1, bias=False))\n",
    "        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\n",
    "\n",
    "\n",
    "class PyTorchModel(nn.Module):\n",
    "    r\"\"\"Densenet-BC model class, based on\n",
    "    `\"Densely Connected Convolutional Networks\"\n",
    "    <https://arxiv.org/pdf/1608.06993.pdf>`_\n",
    "\n",
    "    Args:\n",
    "        growth_rate (int) - how many filters to add each layer (`k` in paper)\n",
    "        block_config (list of 4 ints) - how many layers in each pooling block\n",
    "        num_init_featuremaps (int) - the number of filters to learn in the \n",
    "        first convolution layer bn_size (int) - multiplicative factor for \n",
    "        number of bottle neck layers (i.e. bn_size * k features \n",
    "        in the bottleneck layer) drop_rate (float) - dropout rate after \n",
    "        each dense layer num_classes (int) - number of classification classes\n",
    "        memory_efficient (bool) - If True, uses checkpointing. \n",
    "        Much more memory efficient, but slower. Default: *False*. \n",
    "        See `\"paper\" <https://arxiv.org/pdf/1707.06990.pdf>`_\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\n",
    "                 num_init_featuremaps=64, bn_size=4, drop_rate=0, \n",
    "                 num_classes=1000, memory_efficient=False,\n",
    "                 grayscale=False):\n",
    "\n",
    "        super().__init__()\n",
    "\n",
    "        # First convolution\n",
    "        if grayscale:\n",
    "            in_channels = 1\n",
    "        else:\n",
    "            in_channels = 3\n",
    "        \n",
    "        self.features = nn.Sequential(OrderedDict([\n",
    "            ('conv0', nn.Conv2d(\n",
    "                in_channels=in_channels,\n",
    "                out_channels=num_init_featuremaps,\n",
    "                kernel_size=7,\n",
    "                stride=2,\n",
    "                padding=3,\n",
    "                bias=False)),  # bias is redundant when using batchnorm\n",
    "            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\n",
    "            ('relu0', nn.ReLU(inplace=True)),\n",
    "            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\n",
    "        ]))\n",
    "\n",
    "        # Each denseblock\n",
    "        num_features = num_init_featuremaps\n",
    "        for i, num_layers in enumerate(block_config):\n",
    "            block = _DenseBlock(\n",
    "                num_layers=num_layers,\n",
    "                num_input_features=num_features,\n",
    "                bn_size=bn_size,\n",
    "                growth_rate=growth_rate,\n",
    "                drop_rate=drop_rate,\n",
    "                memory_efficient=memory_efficient\n",
    "            )\n",
    "            self.features.add_module('denseblock%d' % (i + 1), block)\n",
    "            num_features = num_features + num_layers * growth_rate\n",
    "            if i != len(block_config) - 1:\n",
    "                trans = _Transition(num_input_features=num_features,\n",
    "                                    num_output_features=num_features // 2)\n",
    "                self.features.add_module('transition%d' % (i + 1), trans)\n",
    "                num_features = num_features // 2\n",
    "\n",
    "        # Final batch norm\n",
    "        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\n",
    "\n",
    "        # Linear layer\n",
    "        self.classifier = nn.Linear(num_features, num_classes)\n",
    "\n",
    "        # Official init from torch repo.\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                nn.init.kaiming_normal_(m.weight)\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                nn.init.constant_(m.weight, 1)\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        features = self.features(x)\n",
    "        out = F.relu(features, inplace=True)\n",
    "        out = F.adaptive_avg_pool2d(out, (1, 1))\n",
    "        out = torch.flatten(out, 1)\n",
    "        logits = self.classifier(out)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdea77b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\n",
    "import pytorch_lightning as pl\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "# LightningModule that receives a PyTorch model as input\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        # The inherited PyTorch module\n",
    "        self.model = model\n",
    "        if hasattr(model, \"dropout_proba\"):\n",
    "            self.dropout_proba = model.dropout_proba\n",
    "\n",
    "        # Save settings and hyperparameters to the log directory\n",
    "        # but skip the model parameters\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.valid_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"train_loss\", loss)\n",
    "\n",
    "        # Do another forward pass in .eval() mode to compute accuracy\n",
    "        # while accountingfor Dropout, BatchNorm etc. behavior\n",
    "        # during evaluation (inference)\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            _, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\"train_acc\", self.train_acc, on_epoch=True, on_step=False)\n",
    "        self.model.train()\n",
    "\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"valid_loss\", loss)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"valid_acc\",\n",
    "            self.valid_acc,\n",
    "            on_epoch=True,\n",
    "            on_step=False,\n",
    "            prog_bar=True,\n",
    "        )\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc, on_epoch=True, on_step=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898ff217",
   "metadata": {},
   "source": [
    "## Setting up the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737dc1c3",
   "metadata": {},
   "source": [
    "- In this section, we are going to set up our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc714f7d",
   "metadata": {},
   "source": [
    "### Inspecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b81c954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "\n",
      "Training label distribution:\n",
      "\n",
      "Test label distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 1000),\n",
       " (1, 1000),\n",
       " (2, 1000),\n",
       " (3, 1000),\n",
       " (4, 1000),\n",
       " (5, 1000),\n",
       " (6, 1000),\n",
       " (7, 1000),\n",
       " (8, 1000),\n",
       " (9, 1000)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
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   ],
   "source": [
    "# %load ../code_dataset/dataset_cifar10_check.py\n",
    "from collections import Counter\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=True, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=True,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=False, transform=transforms.ToTensor()\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=False,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "train_counter = Counter()\n",
    "for images, labels in train_loader:\n",
    "    train_counter.update(labels.tolist())\n",
    "\n",
    "test_counter = Counter()\n",
    "for images, labels in test_loader:\n",
    "    test_counter.update(labels.tolist())\n",
    "\n",
    "print(\"\\nTraining label distribution:\")\n",
    "sorted(train_counter.items())\n",
    "\n",
    "print(\"\\nTest label distribution:\")\n",
    "sorted(test_counter.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce7e382",
   "metadata": {},
   "source": [
    "### Performance baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d85549",
   "metadata": {},
   "source": [
    "- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\n",
    "- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ad1007e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Majority class: 3\n",
      "Accuracy when always predicting the majority class:\n",
      "0.10 (10.00%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/performance_baseline.py\n",
    "majority_class = test_counter.most_common(1)[0]\n",
    "print(\"Majority class:\", majority_class[0])\n",
    "\n",
    "baseline_acc = majority_class[1] / sum(test_counter.values())\n",
    "print(\"Accuracy when always predicting the majority class:\")\n",
    "print(f\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b19c08",
   "metadata": {},
   "source": [
    "## A quick visual check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2e25203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JQxaFEr33KpSovfLI7zkS1DRagui8RqJP4EP2XBRYLWhds+ssVX23I1WKB0MnK97ybwjEGA4eS4zFS8ljorVBq2Loxfx5CJGhd9xcr+n7MRs6RhflEfYvVqlsGB0gWEFiNjSPtM/eIzsomXvw9vTb6f4OszCPk5K98plsh3wmBTorQaMCRguShBDuHLS/N0UxZifj+0eAEI8Nqf3zp0TY6/R05/OMsuTnViJ7tGDaaaq6oW0brLHM2zkhBC6GZ0gZTzeOVFVF0+TjRARiwFhb5pygdTZS6/kSY0x+F2QlqERlzyEAUSBm5T8ODhGorEUqjdaAtDg3Mo4jg+vpx5715obtYKnrhveefYjSBqPt/pUqpWiahrpu3nkM31EJJjbbjtdXa4yq6Ddr+q5jtd7QdwOr6y3J71AKjDEsoka0pdslgh8JPnG729D3A6vVBhOfUFcWN470ocMnaHRFJUKt8hsJKdH5kY0bCd7TjSPOB1zw2SIOHiFhAKMhjIHrV6/5zuqW/tMvSHWDzFpefPIDfIJXr2/ZrVesVyv80YQOKRFiyh5f0Xv7rSOlI0/woIgmRZLS5Cne9are8AbvoY/lsMO53oAh73lr6b4iTHullRVI3lyO/967HW7cEnEImqwIdVmDd1f6tFbuxE33x2TFla3XuLdkY0p7uHOCmzLMmVefEIoyjIToimI7KL/sEU7PcrT5FA8TCeWe/Z17dT6QlAcl+BBIxfIfR8c4OpTSZX4EiCkvdNHoBEmDUfrNOJ5SiMoKNhuegvcZ/vLDwHa1pTYGazQ+pYO1HRUpCSkJYkB8oh8DGo+kiIhBKzDGM58FKi1YAZ0c0g0HPz8lYvI4P9D1G7bbDev1mtvbW5bzBTKbYa0lhMA45vUQQzacRjeg0CzmM6yONDqhdX4PkUgie7o5fpg346hURkNUQqkJvVCHuaYgpUBMgZhGEgljzeQg5jWiNNrk6+TniISY1+YdeYdN/b4PllIsCmryDvP3zo3s+p7b2w3exTz2MZEke1DamDyHYyKWZ53er5bJ24p35vk0/vs7mGDIo5jS5I3sH6k8k1L5YDnWgBMyFLJxahTURpGQEm4oyqjAmtlLz4b3Psb7NjTpvtwb08nbp8zfPeybDvcoIpTgBFOY7z6qmkdCgdIEH0mimLVzUIokgjUV1tpsgCpDTAHnQzGoFFVdZc9c6WJnHIygaf5KMbymYRegbStEhKpSeB9IeHwYikE4Mow7RtfjwkA3bNn1FfP5KadLS9NWWJ3vftoLY3z3BifvpARTypvMZtvzgg3desXY7ei6niGNbOmR4NBGaNqKVI8oK4xDoh89znkEx9Dt2KxWrOY1bVXjfaQbAy4m1GKOtQptFTokUvCMY0ff9TjnGWJWVj4ePBOvFe2sRSmF9yPr7Zbx1rM1N5i2oT1ZcHt7Q+cin7y4ZnAjQ4GCjp9t/1OCVOBCSZCmWXV07D5OWHaD9JYJel/nHf9839B8m9P+1kl/dJ7pmseB4/vnCX7E+56Ez7amKKS87jdyNNIEe6Z7v8sSU1ZoxxtDLEovK7bps0kZp+xNT0owOfIohyNPMB6e4Di+CSARUqC4AXfuNZa/jSkRMu6ZN0gfcN5jtOT7iWm/0YQQCxycFzXTwtyP6WFzytcoi9d5xtHTD44UEt5HUEKIWQmKMtnbEkEF0CHhihLQgEhRHOLQ1lEZRavAxkBvw723lmNh3g/0fUffdQzDSNtkOLmqa4ZhyB6vd8TgEUn44BhlAAKSPDo6YnLEybPXtsxTld8HCT3ttOrIK9nbW+X9lnsZxx4RsNYczfsJOp3gv7Sfm28NB8AR1Hr8idxRMNP/46QAi6EVixfYdR3b3Y7tdpeNHLIyUTpD3qYoQR/i3el07x7urv/7c1/2m3c+fzw67iBq8v44fq7DOEzeoZJspKcE/uj5p8jDXhElYUIm7+46R97e8TMd290pMcX8pl+rYqRPxroqHv4dfS9vvi9K6EQpDeIRBGurvG6Kl2eNwVpb4NX8bozROdZeZWW2d43L/R3D+VpbtMoe4vRkxhiUAqUiMXlCGBnGDjc6RudwfsQHR4iefuhRztH1O2azE0TlGH6eJ54Q4p13/MPkneHQOAS6zcBqfUUYdgQ30G86tFJYrTnVoNEoB5tbh5fEehfoescweiqdiC6/kNevr9FKEROEpEAbzk5PWM5nvH/S0vUDm37g87HDBRh9YgyOwUV6n9AkdKNY1pYPv/YRVTPj0y+u+f73P+PF5YqzsxlP2sTj1nB1c8Vm23N7vcKFyBjCnQmtVLboYn7/7KfaXgEeTb27e2XZDN6uyPbH/8gid/65fwvHh+03kQJ1Hot3G5yzgIO9J2i4q2KnVTNtBEebw96zTAcIJcW99xZLQtRBCebFMN1zCmoPeRlDVmz4/ab5lSNwZDXcH0Kl9D7hxQdPiAHxCjc6gg9IUvs4ijEmD11ZhCmlvLiL8tTe5zGZELyyQcQQCDHiXV5Q2tRZyY6BYXD5vSvBmLKBqfx8IhBGlZM+rKHvLL0LvLp5hdFbfG2xTcXFvAF3iHWKQF0ZqlqjdGK7XbPerPew03w+5+zsjKurKy5fv2a1usVYyxPxJBmIkvB+Rb+9Zn35ijiOaG2oZwuqeokow6Yb98rLmukdTu9RUKrOyicmApHtbsX19WtevnpBXdf89Dd/Bm3Kw/qciex9jtMoEYxRLJYnnJ09uvPW7i+Z/ThP/xQlmFIilncU9+owm07rzZbVzS2ff/IZ19c3vHz9mmFwpJhjSPO6pZ21LE+XOO/ZbLc4n9GAfhj3kKwcwZoHeFNKrki+pipaSU3G1KSk72kLpQWlJ02T7zaRY64TOqRUxEigkkRE4e55XZPRMCV2xXi4zPEIqnuDeFBk0zUP3qmeDDg4rFcBpTNUKKL27/0NJSiCrSqquiGlhFbFsxbF6HKMtm0bjLEYY+j7Duc8LiSaWZ6rzazFOY/3HtlD4JHdbof3GQWazeZZkRpdUJCASI4rr9cbLq9eslrfcnl5iYjKeQBGE4OHFBAC0Qdev/oChcIoRf3oAsiJXN5nx+td5d2VoIv43tH7AaKDEDBlAw0x5iB1BOsjyY9AYOg93iViyAsrxkiI4EKGlEIib2IxQx2oOc1sxugCMQ3cdp7R57/RJBQJlWKGo4qVG1WNl7zZ9CEwxJjjDFpjqpqYsuW+j03E+xvwMRhTvMDp332ySv5M9troKONQvsxv2x/6JR9Mcl/D7V3Mtx9+dMr9vd2DayDhw0jwPbn/6aQELW8m6kzXn7accob7SpB0L4YX3/AE49HGIqliHEaGfqSqQRvB2snAOIKW7g9QOh6PNy0MbXJSi7U231V5p0LJIosZWpUkeXym2E/Km74PAV3+TptsiYZigiutipdHmTM5O1ErjS+xUe/GrIR1ibGqnBChsDmbVOUs3JQS/ZT9JpBSoLGaK6PYnLR02+6OMeaDYxx7dG8YxoEYAm3TYo3ZK8sp3lFVBpHErluhVY01FSHMGboV65sX+O0GYy3LcIESj7I1wTm0zlmteYbndx6jh6RAV4fZnyLOjWy7Da9evcBaw+OLR5yePaGuzX5zTUlQYlCi0EpjTJ3jM8cb9tHbTNMGPbmUab+Xl9BCyQIMCec9IXjWmzVXry55/eIlr1++YhgGlNI8fXxK2zRcnJ0xn8+Yty3NrCGR0aK+H9l2HZ989jnr9Za+7xmHQ3z5UIoy4SpyhAyUmSlqnxsgKXLsW2Tllcdg76elRFIqo0miUBJLXDCjC1pNntH+IgcFhoBKb6zPfVbn/bhlOUmC/fyXsh9njpO0N5RFph0tkY40atb3dzVzKp6+Ugpj7R5NGcaMQKRUMSXyiQjaGBqt0MYCgiuG42R4HotWGluXxJgU6fvdfh8Zhh3jOLDe3bDdren7Du9HtC4x35jnjkJhtcHjWd1eEX1ku94w9kNWwk2JU/4IDsiPoAQDQUb6oS8p4GCVIiTwKaGSoKNgQyL4AUEYBk+IBpImFIgqxIQPICnhUrYoVYyMbgSBup0hqx3Ow80uW3sqwULyzSoSvmxUURkchhA1297R+8AUfUJpVFUT0YSCPWR07k0lKCSEmDcDOVoYqfx7lFEpR5v0HoP/shGf9vx7lvHdDM1pJ7h7T2/IlzlPe7Pw7gHBD3jXlxKJSQmGQ/zi/skF9kowHcM/WTkm0gHCTKlkh07e4VEKOiAoNJaxd2w3Hc5B3RiMrkBShponY+KtQydZGcrdsUPA6JwIY6wtnuAUlxK0qH222T08ab/xeu9JSaGTJjpPmDaTGBGVvYJIid+kfK+mLLoYE+Po0MZgksGT9kiCwmBKkohIIoaYwwUu4kJiOziMgkoiN6cNYRxLxmJ+hc6NDGNPEmEcB2IMtCWrUiTDwFrrrAStRVRiu71FaUtlG0I4o+9vWd28oL9+TV1VaHEYHTH1DD94qFqUajOEmxKkSAqOhEKnGaCK3RFx3tF1O169eoHWimfPntC0S+pmvi9nyBmRBq1UTmSJOWv2ba/04NsV6PFgn+xXYA7nBZwLdN3A0A+8fPWaLz79nC8++ZTNeoWxlpOzMz784H0eXzziGx99xHI2Y9a2aJ3jglVTs9n1XN+ssMrw+YuXXF1f48ZxjxJwtArylDkgQFkPlOzS8oLuofJ7eFHkYBQLiigph1GSylF4JRid9yujhJim7NXInZsQDlnrxbg6TPuyFiQr3X3WZ/k0pLiHXqf1KlKyo5WUOHnMhh36KPkm7a85SSx7gVIKVeDlvLYDPrgSztD5b7WgtEEbu1e0Q0lOy5JRnylfQRtN27Y5Xh0Cu91mb0jf3FzS9TuuV5ckQg57pICkbEBIzAaxURq0JoXA6uaK9c2aV+YlwScuLi547733y5J/dy34zkqw73fEWJFCJJqcSKBF50EJAacMVgmg8d2IixGTckA4CmjxODxjSIiPKKVyeUHw+bUNjs1u5ItNx3df33C9WtP7QG1tsaAUKo3YGOldpHORy51D3XQo7bi5XTMOIwIMDlY7zxdXO9ado3cBlw7pw8eiUkRS2E+KnK2VjhyyY6/lngd3rMveJunNl5Hn6WSp3HcV73qF+9Ont1/moEzuZpemdKQEcYBCkiJDo8eK52Bd5oscK8HpDqaNa7rWFE8r10wH7zD/L9dHaTQEIQyJbT+Ch3kzQzQg+/D8nTF4m0+d7o15LnvQ++O1KExlkHr6W0VwgWEY9jGQON1jzKkiIjkeESeL1Yf9hiFa502sGAvZaMnQl0QhpoApHkKMPp8/Cik4MrQhuC6XFWzWO7oReqfYDrlkwWrF9WaLG3ZHznti13Ws1mtkt6UbtgQJLM9Omc9b2qamMprgHW1bs9ttGFzPGHqsbWnb+TQZSCGxun6FxEC3ueXFZ99H2Ypka07OH3Ny9pjl2aNc7pASY9eRosbMQOK0PjyVSbSNRVtN8IHLqyvOH21o2jnEnNhgtSGriYgPPX23oe+2d0CJpBVJZ+UaS6oOElE6l6oEH/L7F8Vm13Nzfcuv/PKvcHNzS7fb0XUbghtIfuTnfuZn+PrXvs4v/sIf4P333+PkZMmjR2dUVa5VC8NYoFmDD4lhHPmDv/AHeXV5yevLS/7cX/yLfPLpp3z/Bz84MuKyl6fK/D5O5LizRO5NTS2CKYlUSuXsa5UUUUL2BFNNLdCIo62ywTOEhE+GmHQ5f0QIqMR+Xe4vu/cQQSdNkkiUHIJQCmqlsHWFUoqb9Q4hoVXCjxsSHogsZnlsYrIMg2cYPApLjOClvAelDw+XEt553OiJKu4hZCQrMJsMPjjEZ8PPGrNX1t47Qsn+VUplVMWPRaHmsoUYE32/xY0DbhzZbddMGeSjG/FhxLkhx0YlQ8ukjCTuy7zEMAxbttstNzfXkBQilt1u5MmTZ3gfeP78OdbYN/aSL5N3VoJKZ9xfWZOt7xTxJZBWaUFVFowhWoMKGQvXKlvmKQW0xH3ye0zZE8wPmzeaYRi5XW8wr6/pQoDKcn5xRqUNWoQqeqqhxw49TgZEW8aQuF1vEVEMzmV3uWDe3eC5Xu/oxpHRl8LsCUo4krsezxFbhxys1bd6YUeBq69mWE1vbu6Sikd5uO6RFnmLbk1ffo0v8QSn7L5Eyew8hn3uHCpMSjQdAz7p/vkox2QNmRf6pAgn7y6n9k44fzbyivec8iYhogriGe486/H9THDO9M6ORZfMMkmyh3q00fv0a6Mt3vpspSPElKH2EMLeABLJqeEpJVJI01VJqbAHSX4+pSbIkxIP08TGYq0tG+0UX04l6C8EF0CmDLWcTDMMnpByUoG3hi6OuH44mnsZVtJaEyVRNxUmJoyx+dm0wlhN3VQsFnOcd6WUAURLiSllMorZ7JQYEn4cIN4iux1oDXUNKSApUrcVddNQ1y27ksykOMDekgKVVsxmLVVV08ee7XZL1+0Yhx6tFqRYKCVUJISRflhxe/uam5vLO281Fi/vOABVwLuSrOT3huI4DGw3G169fM1ms2UYBpzrmTeWk/NH/Oy3foaf/Imf5Ge/9S3Ozk5p24b5cobWpSQiZJILJQpfYpa2aVmen3Hx9AmbrmO5XOKc4/XVZS6jSTEbiEfe1aQE79iv9yaqmpKDKF4TgooapSJJEilO+AsYFYhErAoQLaGMh5S9YV+NVRIMpp1lUoKK4jBIDhoqAWvAmoRosFUux9ASIYyk5FESqHXAmoQLuW5VTSjQ3sZ7Ezac3tA+dlqe1WgNKdcJTs+uC5QaUyjJaHFvLKcpKzzlddd3/T4u78cR7xzj0DOVUIXgCPGQLyBl7PLWFku2Z94N+mGk6wfGoRj4koAd2+2GzWaNyHsY8+6tct/5yLoxLBY1tm64vd3SdSO9h8YaZrWlWsxQWjFowSaDCokqBaJzRO+oxBMk7mEPASpDzrCLic1mx6Z3fHq14tn7T1lenPO1R2ekUF6ID/hux9BtUVcrBhfofeKLl6/yhuM9iFAXJb3ZdQzjiIRxny6b0nHUi/1LLnW6ExpEnCbDpADf4untFdsbu7h85eKZfpfenH9HZ7h3+N4Lu/fL/bdvU+6FtSVl+I9isb/1fva1Q/fTiqfCjEPcIKWD1ynEbCmW28nQRSoG0gg+olGIqrFi0eS6tKQyLE6anvag/A+RmeKZHrIAslVcCqQzVC37BJjpq21nBB+o6wEQgg9sN1vGcdgXW5hSLO+9Ly8+P2KGvyRbtCmgqpz9qZWgag2Vpm1rTNl0d12fEzB8YBw9RiJjB9bkjU2hid7T9wMMI1oZatsQ/Igb+oOxIzCfL5nNF4TkaZczYkp0nct1eRKxjcbYOU1ds95uiSmgG13iIC0JzWx2ztNn8J1f+v/S9xv69TWRkI0Oq+jXN+xWlzSNwjx+yuLijNWrSAwRk3INISEAA601PD6/YLE4xfnE7c2K2+tr2nrGo/MTCAqfImiP9xuub7/P97//K/zgB9+9E+sMSYgoUCVWKOqgBJNgUo5hKQX9ZsXt5RUvv3iRYcMEJOHi0WN++ie/xt/3H/tDfPzRx3z88TcIhXFIjOxrG5vFMs/KGEnOISlxvphz+vQJKSZ+6ps/zf/7L/0lrLX8xf/n/53b29tsyE+x7LcYfgIlznx351BKMEdUdwqVDSslFCQ950tJwkiei3Nj6ILFJX1nZRVzbn/5tJ/v+T8dDVECUeX1KSphqoSqPOhIs9BoiZmkIQ4QRoxEGjNglCG4hCZ7r4oMUgY1Abh3kShdymemsJFS2WOvqgatsyIy1mJshS6ZttF7ks9OiGi9z6UIsWcYe3a7HV3X4ZzL4Yij8qXpiUPwxBSnmbFX/lkhl88RXPSsth2bdUfXu5JlKsDArttxu7pFa01d1fdwtS+Xd1aCfvAMesSXVPGIxqM4XZzy+PE5i7OW0Y3cXF3yB37f38FiMeff/f99mxcvXnF9eUVjdd74VGDRVNRGYUzEj54QE4Kmns2YPX7Es+dPEFFcXa5QSmGN5b3334PoiX5EzV4Aino25/pmw3q95Xvf/S7B+33GmBKh0prdCM4nhuIN3tcwsWzcUbhTFlFspi9HOicv8Uf8LIt8qXJ9y9neeoG9/3qkhO4ekvb1e8de5v1/78Ked8853aRwKDLPCSeTBi+/T1OsVKNK3RwBKqOhqXB+QJLH9Tus/jLj4d3EGENVataUyWnbkZi9t1KHqLSiXTQQwbnAMIyEFEkiGQabYoQxFUt3glpK5hzZq40hYo3QVNkrstZQmyrDzSFxs9b0g2PXDTlTUBQJU4yOHEsUPN4Va1cCaYwQPcG7I88DxnEgBI+pNFGlXBg+9Dk+GDzBLfEu0G8GpGTLCQoXA8qPXN1e0Yphdrrk45/6aa5fLvnie99GwoCkgJKI392yGrd8TyU2t9eIKNbrGyRpKu3x/Q43DDRtQ1vNqO2MZ08/JkXDd7/71/j0s09w3nF2+jwnDwXPZvWabljR9dfcrL7gdvXyzpzcdSNJWUDT1pYckzKI5MJ7oxXBO1y/hXGHhA7BocjsNEppnjx5wre+9S0++vhjzi8e47M1RAieq8vLbBwZzYcfLEAE5z03qxUxRGaz2R4lmM9mfO3jj/nFX/xF/uZ3v4P3ntfXVwXMOEr8SumgAUW4j0cIGdrWWpFS3HuEkiLoHI8Lk0GpNNpYtICKCXDokBiTZso/OJRiSTHKpsWWS5uSlPpeKbE4lTA2UBmP0bCsarSAUYlqfopRicYaxljjoqZgGCgSTkUkKkLIxohoObJDc1w/FKq8nE0qhVEo7pOJYsmcjkKBK2NGLMhZ/H03MrqBvlsxupG+77IXuCdOmEg2KI6p7I0aBKQgRk3bMmUNr7Ydg/PsescwDNlrlEyJRwiIhL33OsGx77ynvOuBwQeij9gaqrpGjLDbBUxVUzUttm6zhSFw+uiUR+enzL/zOdW1KfgxGJ85/mZtTWs14HGiSSbR1Eva81Pmjy+YLxYEH7i53mR+waphef6IFD3BDcy2HVprTk/Psbahqio++/QTKJmjIpIhVBG2KR3qunib3tmbX9k7K17XHhb4khqIY/z+/u/h7Z+VI97y9z9MI9yt4dv/doJtvuwcaVJo5eHuKLt07/9fcg8TPHOcBUe2IffPs3fYVIFEC3xSFUuzd+Q5WZKPjpT42+yArxoNazTWWpCEqQwoYRiHgrZld1SVBIlUHN+JLeaYFzMn8ZTNSzJzjBIhJJhI1RURrYTaZsajuq5obI0PEeci3VgRE4w+MhFyeX/IVM3jLyUhKxIlK0+tEnfjy4ntZsPuZM5MN4QQccHnjWTo8G5kWwnj4NiuOpq2xdSWVAshCSFEbtQVNAvqds7ZxWOInvXla4ZuRfQDQiT5DC+ur16hlKZZnjF0kbqa0VSKfutJbkC1M5SuETvnZHHGqr3FuZH15paqsng/kJJGVC6l6Ppb+mGL8z0huDvP5XzEuIizkcpCiFJKo3IGLjFm0ozdtpzXo3UZxxSp6prTkxOeP3/OyekZTdtmPqHg6buOzz77jKqytE3Lhx+qkqAhrFYr3DjixpHZfE5TN2ijODlZ8vFHH3J+dsbl1SXhMhZdNLEdHWpXD7oh3Z2UBVrMMPQB+ZG94iRTpgmZiEFMhiu1YEIipIiKqijYA0wMkgv/93tEhoynGglVFLJWUGlodKIy+UtLwkiiNZbKCE1ds3OK3gmjKzH9CcWJEZ905l29twBTKWnYJ7mJ3Nl7UsqecZS73nMGdxPODQxDRz90+wxP58a9MZ4zN/M4TVyu05pP5fqT7OkQU47v9qNjGF2BVafLp33tcC7rUF+S/Pfl8u6eYNH2H7z3HDs/I4nh+9/+DrXxDLubTJWWIo2x3F6+wncr+u2KGEa0UdS14JLQ1vDRR4+ZNxWr1Q5dz2hmC37+7/gPomxFEOH1q8/ou4HHT57x5Nl7nJyd896HH3B7c83V5SXRbLCVpV1e8PjZB/R9z3o3cPX6NZvVLSYl5lo4V4pOElECoczoQyzu6KXucQ/KJJxeC+/i1t07312uv7cccXTadzvv2xTg3TO+PdZ5l/Ei7o+e5occneHOKpejb9IhZiHoYhXmhU3KCtAFT/AR7wpcYjRnj89yIa33rNcabRJtKzhG/Buw691n2W8m90WE5XJO0zYkidjakkisNmXBKsE2GarURhE9xKRA5xhfpGQopwyD1iWxANjH3nLxtc08t2lgVlsuTmfMZhajhRR7YsoF521rC1ypCYPDB8/l9Y62zkTD2y7HpbRSoHI8TtLIoqkAxdVlngcxRv7qX/0rXF6/4un7TwlkdMRnYhwUcHX1mu1mx/XlinY2wzYVzW5GyMEiPtMt7z16ysfP3ufxk8ecni65OFny6bf/OrfXr9nsrthzh25v+Gyz4W9+9/t845u/kw8++BrPnp/h+jXdeqS2DUnVhGA5P3nMbrvBWs1mc0uKntvVSxaLc5bLBX/t25fc3l6y3d5QV3POzy/uJF6lpHAhsetGEE1Sns4FNv2AT5FuvWG3WbG6vsS7HkdidrJgfbMhusDTx8/4+OMP+MlvfpN2foKpagLw+Sff45NPvs+/+Wf+T3z04Ud84xs/wc/9rt+NrWuqlPj3/8Zf59WLF4hS/K6f/3k+/trXiSnQzhs+/vpH/MRPfp1tt+Hb3/nOfqodeG/T3m7cd/e4u0JK1iX72r4cXlMolXPYVUr7kplIgyenpQVtiKJIsQIMiEYXKzKlCJr9PUx1fUlyAp+ODoWnUYknjeK0TrQmYdSISgGVHNAXbqhAO18SpKKuYLUdWO08uyHSuWzshaQL681BxtFhzSGLVhcD8jhhaF8KJRNJuCbE7P29evUZLjhCzHW795GqzGebzzf0Qymi99R1DQn6Pis5AbSt6LqO9XrN1c2KCBhbT4PN5Llm6NZkB2s2yyGt+8xFXyHv3kpJa2aLJT/1zW8yO39MiMKLH3wfCY5x5yC1mTIvBbrVCkaLBIcBKq1oKksisBwVlVJUWrM4OeX5+x/w5OlTfvInv8bVasOnX7zCKMO8zYTY1iiMJD5+/ozdcsGT0xOWVYU1lovHF2hj6MeBn9v1vHzxkqvLa8bNGhsDTfQ03hEFKtXjIoyS7k3oo8zHIyvsKGfhDbfuR2p382Xj+SOc4wBZ3vs9k5f25ucThHOAd4ACG07P82VX39fZlMClSlP0QpHCgaptYjgbhwz5DYPDuYA1houzU6rKUtV1Ju9SAVtFxiFnTn6VC3i4u7sHCTBftMzms5wkoDK82YamwJxQNRatFcpogsupHlVt8F4RYvbKsoUNxqh9UoWxuR5PxWzsaWOwJJpK57KH5Pce3Z7mLkJOrCiWcBKcTygJ+ADOR0Qp2rbOEJ9EKiWcLWqIke/JZIRRqKF6hr5jCDmbUCm73xwl5ow6yIk+URJ0UmqyQGvPSmteiSDjGY1WzBcLLp48zXWFl4mh3+CGHUQwWjGvap49f8rjZxf04w4XRwKB3dAhIWd1BtcR/EBKoRADRPphQ9POaPWc5fKM7PlbjIpUpr3z3lIqdX8Edl2Pj5EvXlxydXWLVoq+G/BuxI8eDbhoaE/OGIdIsp6PPv6Q9z98nyfPnuaOECESUsAoRVvXPD4/Z163qJjotxuSdzg30m03bNYrQkpsNrlOUM9naJ3JDJ49f8IXLx9TVVVh4ImHl1E0m3B4QffXqkwx5KkLSVaB2YBLIKHHiqPCoZIFUXidU2W0UignBQbV6P16VOU6OTksoxQZWrWpow0rTqpIY4WTytDogFERg2YiowCXQ5wuYLRB68SyqdHK0FSKjdNsuoRPgW5Ub3iC0/o/NtDvK8EpTqiLFRCCZ9dt6ccdPjhSytmp09/lesGDQT4p0pKqSEwp0wHGSN/1eTxF2G47ttst6/WGECPGVswXC1JcE30uIYohlhBYS9vOWC5PcubzPQj7q+RHUIKGdr7go48/Zvn4KaML1EYxjg7nHdrq7O5Lot9tSc6gYi6or4ymrSqUBpcCja2oqxpbzfjww/f58KMPef78Cd3o2HUdtdbYqmJR14UhJvD88SP86ZLd2SnzQqB8en6OR+idYxTN7OSc2elrVi9fkIYe1XdU6xUxBrQa6cLbIMU8/XI6cPkdvFX5TRPkq5TXu3yer/ujKNG3w6HlRG/1BIumuhcTPP66c1f7vz/UUGUFmFJZ5Cl3C8i4vuQ4V0lacqPPsbd+pOsGjDEMo8fWFbauaVMC8SjtSGOOwe6z097yrAcM9o3bZDZrmS9akIQvrVuaUBNizsCrapPbKxmFqMydWTWW0Wl8UKRQymCUYKw+SqqZWGhCTgc3iloZGquwCoSwh16mdIYUSnbpEXTmQ4ZlVYj4EFFKaJqq8EhGWqs4X1aF+/MgmeViZBh6Op+TZiorOR4YPBLHwtQfGIaICh6qtGfpcQZ2G8V1jFgCp7M5p6ennJ49whpDN+4y5VTfkcjdOOr5jIvH55yen7AdNgx+wCfPtt+hTO6m4V1PDAOJXEaidWJwO3zoQQKL+RJBocUwa3Qmoj96rylS4qyJkEZcCCVzNn82upzyb5XQWCBZqsUJ1WaHioEPPnyPZ8+fcf7oHJGctBSCxxrFvG149vgJi9mCShmG7Zbk8xiF0o7Hhcg45rFLKaK0xRjFxcUjHp2f5xKCEAgpHJTgG0sscZdAuyiKkms2xbUEQWKhp3M7lHJYHDotiChiYaKRCF7ifhWqCUycdC5T9mkqIOOAjTtm7paLGlqtqHWFlpAh+6T2CiV3YMlUgSYMaKWYVRZrNLPGYl2F6MhqNzD6Arfef67ylVllDj9PyksphSkdWmKKxODo+x27fkssrdBEMjIT490975hY41ixOu9Lgpkr3qdmt+v2X8oYjM51hv2uw2tNO5sRC5G7MS3z+ZzFYpGNpTfqwb9c3lkJLk5OqdoZ16sN613PMAwQIkYptDHMrMFqTW0E5XNt1kIJUlvmSnFx/oizx0959sHXaGZzxCicBj/2fPLFZ3zyxSvW2x7vRpazBqsVLjh88lg0TSvE2QyzaKlsxAi0dc1oGvoAQzWnVy2jbhl2PU5bghhUO6cisPA98wS9V/xgij/nV7OHQw9ZWZPSOQzkNAnuFNu+ZRP/YXDo2z7/4Qrx7Z/v/y69qdju1Drd0SiJQ/7rsbubv/bwRfmfAMF5whjZbUdIhhiE1e0u9/0ah31SiRJdWgxFPv38C6qbmrpt+OCD91HKMvqIR+UUcd7UcT9MhKxQ5vMWbRQuZnok511maRGoZhXaZCVofKRqsoKrKsVuaxh3rpRSaOaLtniCispm4l/vA5JyxLPVCltY1UKMBSov4xrzuEQXYZziHZqqmRUYC2pzGH8h0djE41PDo0WFGx1TbSokdrsNq5VBbEJX2chLNrLtNnTbLRIdq9s1r19cEUKibmre++AJZ4/OWS4WPD87R/lIGLaMO8vWj7waeyptMO2C2dkzXtys+fy243Sx5Mn8jJ/65s8wDjs++/x7dMOAG2AcE6tXt8zbJRdnF/jxNdGvUCrXDc7mFSkNbLZXjG4gxOxpG31GpRPbzerum82BMXIZtuwzt6cCeWPr3KHA5ISglDwkT72cMTOan/nZb/Hk2ROQxDjkXIC2qYiLOZUWfvH3/X4aW1NXNZtXLzHWULU1v/h7fy8uRFCa00ePmM0XuRbNe7xPGG2o65rZbIYbcl1zNr4OaypN/75lfSqVUJKIKjPHHLAST3I7Xv3gL3PajLRLsO3HJNNifU1T5tPgE8gM9IxBayYz6rAiS0IaCRM6muEls+tf4VkYaUbFKEvENChjqW1LVJqoNGo+B2OQKhMXZEN2h9GWxlgu1w7XC0TJxBP6rgqo6oqqqvYK8NgLhIOSTCR88Dg3sN3dMow7fBgypCs5rp5DJ2l/rkkyc9jha+KBFSXMlotiOEU2XeaItXWDrWuU0qxXa8ZSA/ze8+e5fZ8PvPfexzx58pSLiwtS8SzfVd5ZCVqtkOC5evky1+G4EUMqGV5CTWamtDERxwA6ocVSVxpt4OT8Ce9/+DHf+vn/AL3zdMPAi6sXrNcbut2W5fKcpmn4YHGCuJHgRrbdFi8R5Qa+eP2SKIYxQps8SWtMjGxHz84LWw9eW6RpcQhDSIyjzzWJSqgrS0XawxfHsTnSFIuatqTJAntTsUzyZR7frxYq/VtShG83YHm713f88TQOU1LyFOQ/BJZThBQSISS8C2jJBdLGWLxzxRMJmdPTqMxUYi2j8/TBI33H4uQEawHlcjZXwfPTHd0s++SS4+e9f/faTFyCiuhzqx9tclxsqk9KULg9FUlpDIZUSH5HO2YlqDTtLMcE8/yoUTrDbYSIxMRMC0aDrYQ+JgKhLPI8h1QpnzB6Sr7K95j720mBqPI7UknljhJaY7Qi3sehiq5QStHUNbauaJsWJYnKGsLYMQ4jSqvc4897FHAym/H40TnvP3lKGkbCrudkMaPWmclFaZM9L1XhMQxR4zEEND5ExvWKqAWXYBiF0cH1ZkdMgVmjSKnH6EDT1NjKoLXC+4GuF8bR0ZeqiuASwcIwbO+8NWMrdFVg3X39Z+FoERCda92UJmuTmImQm6riZD7jvfeec7Jc7OeyZNAw96OMkcVyQQqJ0TleffEZprIsT5ecPnrM3FqGQqo+EbJPNay6IACH5rR7KOTuEvmSdTl5Oiql4rXlRCpxHbhbpH+BlkA1M8iwII41ww6aOiNm9S6AzIhqRqerHCecCM1TXnO5JlYwssOMK8xwg+0HbFJ4cYRkCRh01RKVIeoKcwGqbRCjEWK2P0QhKmWOWefxXpGiZeqBeDwJ9dSGiiOFt+cMPuxvMeYOQc4NOJ/r/SYDcQopxeP44dF4xoKoTEow3jM0JgJuESk8waUFk+RwhNYa0TlxRtcaaRRPnz7h7OysMCx9GRPV2+XdOssDtYI0dHz2K9/ORe4p0ZDjJ1YrKhF0SpiQiL0nao1qG2qT08ofv/c1vvGt38Ev/F3/Ib732Rd88cULvv297/DixWvWt7f8zLfOefLkCe9/8DGffOc7XN9csbp8BSbhJfDX/ubfwEfBJcXX3nvKrG4YgnA5eNYOPl8P7IIQq4YuZJaS3WbH6H1u0VTVaJUwxt/zQCY4sSyydLT9Hn64Ywl9mfI7Pu5Lx/JL/v7dFeebivmtoOIENdxThHd9sIMHOBWXT81Jp/sMMRF9IjgIHkylMbriZFFl6rtxyPV2JVYwX8yxVcXl9pb1ZkfvRqq6ZjavmM31PnaXFeBhfDP6LCUue+zB3n1ebTTGZiXoQo6RGaMhUhaWQ2JmttbWoEVjG0tlLW7u6HcDmuwJWqv2JZBVXRVOw0RyHnxgXprt6koRekiFxX7K6LPmUOs1OI+PCZSUfn0Klfy+aF6iLpCo2Xd3vPNcOme9NnXFyekJTdvSNktOT5dE79ltb1FJWN9uiD5gtKI2lqcXF3z8wQd888OPcLuefr1m1tR5PPpIiLkljouaIVkGqfCqYgjkVmgbR9SCWZyyHRLdEHm9vibEnrbyuTC7SpycLjBWoRR0/Q4YiFEYYq6THDrHvLWsNtccv9S6btB1DRxqwaaYEQJKR5SJKJtJEKITog8sm5Ynjx7xja9/jZOTJaQMm4okiJ5h6BjGnvnshNXtmpubW/7KL/1Vqrri6bOn/O7HT2jaln51jXd9NkjEMnUwqaylqo5ZRSZ052jGfcWaVCpTomWqxUxFpongb0n9C2z/CY0RZmkGO41zwub1jvmyobEG2zkiNV5anJnhlSYaXZRSwncjVlVYZZg3Edu/RvdXaDWgB5DtjnEbcSNQtURdEU1NMyb02RIqVXpkanRVg0oESbgh4EYhJlO8tcPuIcKBeGAvB8hy2hcge2rD2DGOO4Zxy8Rkky3QvKdkisJs7B7CK3md+tIOLxYWp1CabIcA1laFu7RC6aP2VykvtqquciummGibltlsxgcffEDbznKRfDoU8r+LvHuxfPA03tMo0DEP3um8zZlIIlhtQBuiqYhVTTIW3Z6Q0IgyYGeMHm5vb1jd3LK6WbG5HSAY2nbJT/7E1/jgw4/54KOv0Q07OhzDZyMagxLofOkm4SKbuGBMc25oufUju37kdr3l5vI1q+tL1rc3DNsN47jLMUmlaeuKDDrctRL2YQBhD4uSJuaMgzd4jIsf/3s4zz1L5kvkVweH7o9869/dV3RZjqnUjmDPiRUlV4cDOb7nXaDf9bkhptLUVUOKQvQJvwPvcoeE3jmUitR1TdM0KP2Ivu8QyS1X0AlfmsoarWmkZrvZADlJRilP7hWYOPRRTNOb4U3Y9j69QSAmR/L5c6NzbCgvpphJiLVCJwW+cMKqxKxukHpGmMVcN6UkdxmS3BhW29JZXkB8QrkImx6RzEG7rDU+BUJw+AA+gp1BLJ5QvxvxIeBC7q2GCG7MvI4hRhQ1tlLYtiJKwB2VEqQEq9WaqrY0s5Z24VHKEdyKfrdj6DpevvyC1WrFarNmfrbk5GTJ0+dP+Przx/zE0wue1ooRzY4apStCSAzRM3SewXtEKeaLU84fvcftak3vVuyGyLP3z6nnLSGo7BlGGGOgD56d92hJ9N7hwoiPMHqFqWpSiRGL0UDEViPKOJTu9s8lSEky0vu1pVKEqDJkrRTK6oPVX3rMpWHk7NEZzx4/Zt62kBK77QYJuWTFWU0KHq2ERKB3A6tuy/dfvGQ+n9GcnOBTIEkghpFuNyKdwjYnmfi8hD5yreOUbp8RgRzfLd7qnSV1QI+EnPVpC1SYW2olVPD47Qu4+TZfrzrmpmIWIpv1Ldvesb2+ZpRTYlPTbAeGMXcm6aNh1JpQGbQ1pJgYbtfMtUEbgyxbgu/Y9R03IlTWMNaFcs4H9LjFq56dtuw+FfRNTXM7QzUNummYPX8fsziFpsWYlD1gbdHi83o8mocx5M4gIQSMUXvFlY3UTCLhvcN7xzBucH5gDLnnZJxal8Uc9xWyMZhLHXxpdZSNJudyAtN03c1mSwy5EYNCoWxF9FlZhhBoZ82+kH+33eFCxGOYnS949uQZRqu8b6eAkgOd27vIu8OhArUkajLMrwGz78QuWFuTbEVo5qiqJdkKU8+JE1VWykk069Wabtcx9LnZrhJLZTWzdkbbNjRtRT1rqNoGU1eINoiqiFLlxaIUjpqYKmI0dMHR+cBu17Fdr9jcXOP6HFuM3kHZXkm5zkZzxJV3LPsEkkm5TNQ/YZ8qDLyhCPci0z9yb/HcK7SdvKC3Xf8r5E1wcPI870b33v6XE8x47PkVUm1ReBcYh8B2PWIrwVqNLtRkKeQWJjHEXAaRIiI+p45Lyh5MXRUlmC3rWApfVWEC8d4RAqRkctD8DeV8GKdsMR7f812J5IU4sXyIorRkyYXLKdcXI1MhbTY9UVZldomChmsBbfP9iRaUySnvonJMW+mI2w4lLmKwVjL1X9DokNARxFASPHKijfe5wSiSn1Dp3NEgpFggZIVYmxMX7j1bDHHPPDO1gnExr5ftZsNmvWUcPVYbTk5OODs74fT0hNNZy7I2VClzVlZGlc6N2eIe3FRbFVHaUNUt12FFHDxm2/PIRaxPjLsRF2XfYDqQGGPESK5rE5Vr84hCP/T7eaRFIyqhVO5viLg7z5XpuhRxny2Y33HOXNSIUfu3L1NcLHjqyjJrG0gR7yLjOJCCz3ydweSMzhjo+x2jG3Deg7WItYg2jONA32vGsSvNdRVRqkxPZ3QeX3dcc3ZsGU9TMx15Nnfnq54yQ0u4RSATEvgdjCsWJtEoUAlicAQ34IctfqzwKhHdQBgGfD8QvCZoRXAGbIaO2a3RNndL0dVIio4xBnapwifNiMqIRorgPZ5IpwJhu8b6Hh06aBpU26KXJ0RToUyNJmBE0CqCePSREoS078M5UaBJmZtQeHijw7mecewZfZ9LIUoIImfYHxwLpYqhUwi89+hUjLmEIWVS7xAiQz9k5iJbE6qAUllZhpAL95kSakh45yAKddUwny1YLk9LKUuJP+ofEga6J++mBAVOasN5Y2m02WcUudHlVkUo1PkJanGCPn2Crtu8cYjOTP/B4wfH6vKWT779A247T7dxKGloainFsdB1PberG0xlOTk95b33PmA7QJIKWz+lmlWgLKMsSEETUmI3OrZdx9WLF1x9/hmry5f4riMOHWnsCHi8JHZdjvFEL/f33sN+O018skUUQ2DX5WD8bNaizd3Gc/n/Ux1ROlKABRfYb+aHC+6hgTKu+Zd3rczDsUc/3SEPlf1dHO7mraDove8P4Xshe3xaW/rdlvVq4PWLnpOTmsW8xjLD6ExM7NVNySwc9hDHMPQYa9BW087a3NnBmNL01efMzBhIMXBIoZuYXb5Kjsfr/pG5FZLzUwp2htCUPoyKssWiF+hHlxcZAUkhj6fKLDcBqEUhWmGsQpsMcaFAq8xBG6YGy6Kpags6EaPJ3eVjoi6LPUWYNbrEOIr/mhKjz57hGALB5y4SyWqch1HdS0iocuB/GAa2uz7/7dDz4osX3Fxfs2znnMyWzB895fmH73N+esLH7z3hrGmpfMhJKmXohtExDJ7VpuPqZs2uH+nH3IBX6Zya7xOMHvouEGLP1eYGmc2RqsZWFjGaIUV8ym2Zm6bldr1iGHqcC9R1TV3XhDRk76ICJJC4pwRVKkQJeg9OiIA1NrffMVOtWSK6EVIg+J660bTzitvVLRTYPQSHVoq2Nvh+S3Aju2Fk5xIuRX7iZ3+OWVNzcX7C5eUXXF8FXNfRtkts1XK7c/m+m5qr6ytubq7p+z57IImp6QFvztBiOU21tpLjzdbkLEStyDWgwePiQIgDdV2hjSXGbGymCME5uq7HhAjDyDAO9G5EBYUKQggCo0aLMFeJixpOGkCPjAR2kri1FaaqGKxhaROzIIQusE6Oz1WELjLrFfNbGK0mNjV9baiGjmrYYQahCQ2tBIL2NKq7s85CCKXsICuqmAJ9N+TuICI4v6Pvd3T9BlSBMo/+XumcqSoCdTWDNPWeHPH+oPRSDJlYexhZrTasV2tAmM81Qz9mZKrvSzmGYrfZEn1uzJ6iYjZb8rWPvs7z9z7g8eMnjKHPMfmQCdnvt3H6KnlnT7AS0CnS9R2uPIyIxp6eU51dUD//EN3Mse2ytNZQROdIPm88osC5xO3llttuZLfrczfupFBRuHm9hqTxHlbXuWgXP8OgiBiGnUfXBl2Rmzkm6J1jtV6xXa9YX10ybNaEviP6saSWB4zO3SM2w8ggORX9eJLHEuTNpXA5JqgE/Ojp+56b21uqusKWtiJKq7KPZks4khfwFJtLITJ0Y6lBE3TpoXegbJsSQKa5V7JO43FzzHJzhXg6ySG+N6nK/SEliy280Un5bcqmpPYnIRSLLMVAVTcslxW1OaOqG6ytqauWFCKj9wzjQIieqq6om7qk/Tcom7Mwtc6KRylBx1w8bsXm4nTxNDObSxe+2mU9vvX9P/cd5Py8WZnHwtZxDKIe6jxTvifJJM5xIukNhe8wBhpvsNbQSoUpSAEKTEyoML2zrFliTKAg7uOZ7K+NkNlxokIn2StJkYConBAzxTpHJPf/s/Wd5+qHHtMZ0MK8n6ONYn6y5ElKLJYLLuanLNsZjxYnzOczZk3DeTVHBxi6EUcgiCKIousdu93A9c2a1XpLNziGMbDdbdl2W0Y/okXjQmSz7dG9Y7PZQu9QVUV1miHIMXoosRttbOZ9TbDrdnktKMFU+X1ok0mc5d4L1ipDn1Jo6IjqkDAqE1qQEyBCEKzWLGYt83ZG2zRMNFsh5aSOpMCrUAiXHSGN2etWMGsNRif6YY3vB1ThQo0JqhjZeRhDxRhrxrEneEeK2UCSPYl8uf+c+YJojW1nAIy313efS0npF1g60fiRYAVfWW53HuMFHUZocqzKaEMSjUdYO5/LN8g3r8kZ68oHtCgWtuJsPuPRskFmLX1K1CHSLBckrXDB03syPWHbMiaPS27Px2kT6JB5Pcf1Km8l40DbK1Js6eOAGwN2uHpj6eVeFTn5KEbP6EZSKtAlmeg6ycFcPYR4pHh4ZSWWLNIQc0G8D7548HkBhRAYxmwYWGuxtubs7LScJ5e2TChciiEjjtWM87PHnJ894snT58znS5QyVLopcGtEKZtrbN9R3r1OMCaiD3RDzzAGQoRqtsS2C+zF0/xVNVSmxki2tvs09RXIm5X3ic26Y7vr6fsRQkLFzEiyvt5CFIJPbLYDfT+SvEVhUGLwLoJOiIVhGBicZ9N1rFa3bNcruvUa13ckP5KiJ8Wpa0DezPoQGFPE+XBnY02ly3JUCYkHHkDnsxLcbXeZxmrhcwGzllxYXeJHe79FcgzR+8h629HUFms1jTHAgbbtbTBf8IHgI7p0BMgdq8sx6VgBxiPIMJ8qFcs1xMix2rvvSx2Ycg5KIhTIt7IVRisW84bcskRjTE7jj27INVSSaNoaU7odNG2DMpl7MMSQr53I8TgSBk1I2etoGoOt9N2kgy+TND36l8ChqXQhmbL60n7NTSHO/ZjkhDJBpWwkxJDjysFnyDEkS50i2mpizJvaBFebmApEV6CYUmgf96UyBXaFTKGqS1wL2dNKpQClRw9S2u64CGI0Udmj58vsGcZqROe2M3VoqJqGRYKmaXh28oiz+ZInyzM0UGnNTFXgI2N0IJGkDVEp+sGx7QbWmy3bXc8w5nZiu35H1+0YxgEjBqurQkIcGLqBMDgwhrOZJdoSA/UZCs85mXleeudxxuGDyW2AJCdT7A2CI1FaFSorDWUeKbLXLSodsixVnitGK+azNmfIWrvPJkzlHZJya52YQmnMVBJsRKgqAyniXI8btxADVhS2GkAZBp9RgUjAudIkdiqNmEgkJIc8UDrT7dmKar7IBkxRgsKkBHNMUKuEBE8KHSIBFKwHnxPxgqa1bZkvuU2XSzC4gIsRz6FbhUoJiZlurWoMbVUxnzXIYoYuxONm0eIAv9sxWo9UCadsfhYHqRjyGjApkUJg2G3z+hxHqqiI0tKoCD5ihvWdcErax0tzD8EQPC44YjEKRWeygmmFTgb8BH7FOBmp+YipWD57mL4oyaw0vQ95LjnPfL6gbVsWiyVudLl4PoRDNqoPOXGsXfD4yTMeXzzh9PScqm4yP6sSRLLnOjV6fld5NyWY4MX1Dd2uYvARYxvq2YKL3/G7mT1+zuziKU1JMdda4Vwg+pw2X1kBYzApFw9fXa/o+r6k6g45uBsTrz59ye0XV9TGkFRFEE1QGjvX6EozmzUEoxlT4Gq1YbPtuLm+ZXNzzbDb0K9viOMuV6E1BsTgQ44v5Q0/b2BO7kakBufoxjFz4U2FoSmw3W7ZbDZZwQTPZrPl6uaGlBIXT3Pmma1qRCZAwBMDbLY93/6bn3J6umSxnPO8ukDIi3afEJNg0ggpJi5f33B9fcvp6ZJZW7M4mR3tJWqPhR+Y7It3Sdp3fu7G4R76eZR2vFcqsRDVglIWrXKcqm5yRtUwdsQQiRFUPae2gmkUSrdoNefR2aOc/p9bapf4XMyxlRiyly0JCXm8G2OoUcwXFdoId1o1felUS/f0313F7l3MdV5WFZ7YVEYpv7uw994oDPnZEk0+EnzCD57ReQbnGbxhcJl6rSrFuMYYKiWZkFjIXSBWt0hsoTJIbTLkmQIE9ps4SZerCT6MDM5xu9lmAyfGvCjL+67qmiEcvI6UEpeXN6y3G6rGMj+ZYSpLW1oLuX7g64+e0mpDgyINDlV4WAcdMilybcBokhZeX264uV3xxYtX+Jg5OnvnuV6tuLq95nvf+wyra85PL6jrisVsxmI257s/+D6vrq54f/iY8/ce83T+HmP0DP3A9avrTE6uDKdnp8QUGMaeqrYYZQp7R7ibWShQVQZTGXwEo6uiQCilKSoHZ1PKCQ1Emqbi4uwDZm1NioFhHHKyljYoU5FioB8LG4pVNMrAEJDyjpWknPk7X2ZP1VQoU4EyqOhyh/gQWK/WrFYb+rEQmSuNtgZTVRhbUc/nuVtC02DqBjcOrD//dA9NGJ3Jv1OKKL/D7W64/MEvs7v8HsPqC8LNbSmh2dKsNoQE28GBHrBa4wdHSIFAwuL22cJWGZIoOu+5HUZkp4g+QNMiiwVdSowx0LmBVGtibfGnj9C3a9rX19gQaEgkA6YfSc7B9TVqtc6NjytBNQvkJLLa9txsXt2BW2KMey7O9Wad+weqw3ochm7fom0i2h6dKyQSFMNlUo47vPfZaRly6zBdqAm9D1xdZhL3s7NzPvroa7RNbiK93qzZbXcZbi8xQWMrzs4e8dPf/BYffvg1Tk5OMaYp7DClGXYhrveJ0ubv3eSdPcExRoYyQNV8Tnt2QfPoKfXiZJ/SmrOXw77OSk+medmsY8itlVLMQVEt7Gl7jI+oGHIRsiSiNkhdoyuDaSocidGNDDGwXd2y2+7oVit8v4UwYGuFqmeoWNFt1/ixYxwH6qrU2lQVxNw88zg506eYkxc4JDSk4Bi8I8aY6960xvvAer3BeY9pZnSDx9ieGLIVLjqSmzsObHYdpq5RVYUL8c2C0ZT2SjCExHrXcXW9zjVdgGlsiT+UYlWKKkjHN18YJ4oH6MNxgPvLJO6VRo7fQIxSvk8kcaQyQLFAQVopqtqgVd7MpvsIKZSShMDoR3zwjEMmy80M72lfz6f03WJZ7tieb5Nilb/lmGF01KMDUdnqPfIApzML2UubTiFQCuQTQUlu8ppUTqKh9AIUIEWSgEaRRHLKe/SkPpNf4wwS68MTyB6xRgrUTEq5tjLl3oc5MyLt2RkSOdFgYnqZRGvNYj7n8fPHmf9QhM16jRuGfeuZFCLBecRHIjAOI16H7FEBhFxrtuscu93IZtODyh6EC55xdHT9yGbbIQy4MfLo/ASlYNE2mNL0NwVP8p6UpriKIEmhlUUpmM8rnBsYXU6QyR6BEIMQj0IxAvuYTuSoM0sxHEoSbTYGYsRaw8xqHp0tqKvsBXqfuwUopambGZJSgS8dJI+OjkoFlIpoPbVliuVnTdXOEW2zt2ozmhNSZLPt2PUD2tY5Fd8YqrbBVrlNUNU0aGNQRiPGQLobY5qeCxRhHPHjls3tS7arK/r1LWFwuTZUIr3PjcOHkPcaLQLdkGPHApVMXSgS1mR4NQLSawYNYdujG4d1gdnZPMetQza6x6RYjz29d/gYqU32itBCGkqj5xhQIlgRDDoX+etAhcem430jJ7cprXHes+s7nBvQRvYEEM67fUZ1TAkfYskToHjRJZ6YEt7FAlF6Jl7WnCQT9k12m6bi9PSM+WyOMSa3W3IZqTlORqyrhvl8yfn5Y2az+T6GDiW8UfZvZXROVvvhmNNe3l0JpoRPEQ00J6csnj6jefyMqspQmiqKLnt2uc+cVkz1wjltNmT3N8W8IIwqcQSJVEnQMaFJ+DRACqjGYmqLbmt2RLqxp+t7VteX9NuO4XZNTCMKT7uw1HWbC5E/G+i2MAw9CztDG01tbE79vUen42PMiQvkzT/FhBv77N2kyEk7B8CFyHq9oe9HbLNAmw5RQt9vURqMFep6Rj8ENt2AnY0Y5xhCyDHDcPAE9xh6IsOnm47L6xW2rvKiaG32HAGtTfZQC9530CUT1JvhHBfcl7z4afNNZNeleC4ZDMVPmVeS43fZKlEEYt70xFK1Fi0WpTOEEWLYe38hBfqhY3SOvvQMSwKzWUVVaapaUPqYI+ZdYIovmcAJun7A9jVog5gjSBIhIqiUax1zbKSMiCi0zh4IJub0dqNKd21IKZRgvRAFLJqoFYYAwZG6keAjyVpUyD3TlMqKMopkZVxS/feEvhEqkxuohiS5zVcqEeGYiHdMVaFuKs4vHvHTP/3N0m8xsLq9Radc4kOIRBcIMqKzTqSPAUwpCwEYIUlktxvZ7kbWm102/lRuudSPvijBjMSsZMuTJ+doI8xnNVVlmTU1OuXQQCqGVU5zr3ILHyMsF0v6fkvuYC553GLu/ODd3VmodSYmV+TNOCtGyrqXvRJMkmitZTlrePL4AknZUPbOZVhSG9rZInvUEYLbEv2I+B6lcieFKJSQgSepiDKGuj1FdCY6NxIYxpHNrmO1zlCxbVra5Wn5d54bJpckrwlBiSkQ3eTp52mstc5Ue0R8GHD9mvX1Cza3r+m2t0SX7R+VBNvnGJoTDbsOlaAK5OJ4rcjAeH6HNuS8g10M9FtoQsAPDlvtmG863rdgKoN1nuByScu1CL7L8K6qZ1lxaw3S7dsXaXJmf6UVzgjRJmrlqeSgBEVyDZ4xJoeCdjv6scMYsNaU7N6Qa2fRuWA+RJwL+3cZCHv485h1a0Kxclis9NhEaJoZFxdPMpIQE+v1mr7vGcdxrwSVUsznc5YnJzy6eEzTzjDGopTel1D4lJWmNeaN0NAPk3cvkTCKurHUVUV1coKe5wJWgi+eQbbeJSYMk0VeILOYXVqtBN1USPB5UQNGNCYmrI+M/Y5ut4LPP0HNZqSf+3ludlvGGNn1PbtN7hx8+dn3ERHaxYLlxYJ6VjE/aRAfiG7EDxsWpy0f/9SH1AISA+N2h+s6tqtNiSfllxJLm6V9CE4ytZMoha0qrM0MKUonKlPhVCD5UAx74ebyhqrWLE9nBBNLKYFn6rru3ZgXzx3qoGw2pZQyRFdiVKvVhhByHd3oMgWRkql+TWFtRVVXtG2z9+hSYcLw/m3ZUOnO9Q61gwn2fa9zqcOhtxnk1OVi3SnJxoWqMhWZFiQI3dCx63bsui23q1u8yxyyTdsUOqoGbSNKByibZb7mjyjp7rfDMLLr+tyHwoDSQlsbNLk2MBftHBEgkz0OLYJGaE2Bl2O2yrNRMGlSyTsXAWLgbJHpqMwu0oXcQV1SLjUQpUlKEZTglRBnkaglGw860ShNZRUhmKy8XI5RR7L3f8wYIyKcn56yXCz288JowwdPn5OcR4WUmxMXpNhnJm+iL1a3RPr1BhGLSE0/BPres950aOtBYDt03KzWbLeO2fwEUt4QY/Ks1testzcQcnz07GTJrK7AOZLP122aGZvNDbvdyGxe47wnhGzJi7IED59/dslnn764UzqkdC4qz/yOglHQGIWtLEobhujxLuGT56P3nvHk7JSf+PB9rq4uGYaBm/WGeRLmusKUpC1rKrQ/zXG4bUfqBuLgETHoSmNmFbs04IgEfeh8Yk1i01/z4nrFeoykZsaHP/1TmLpFGVNoa3IJj+xj7gkpa/BYtC4lL+RYuvUL3n92wUtZgYzc3HQ5vpcKSiAQJ7QsSfFWpFztUB7SB0+KAilytUno3UgVYV5lrl632qIbiyWg+0AYI5tXa7xA1MLJ/IykDXoArS2xSqS5IojGiaaZL7DLBdXZEtn2JHOYhwlynatWhJSdjr7foU3Cxpwp3rZtJqjXmkZb6jrRtrN9ApNznpRGnMuEBrnptcbarGqGoafrOvp+YD5fMJ/NqauG9XrLMIzc3t6w2WxKf81Dedqz997j2bPnzBZzvM/JOjFFttstu92WfuyZ2GpChC9evHjnLebdlaDVVE1F3baINbl3oBvztmqmfTS/dE3+Xsg9waR4WbnOL38llT1DEzwmeszQM25u8NevUaubHHMYBtabDf3g6Hc7dtsN2+2Kbp3LKNpFTTOvmJ3Mmc0qQj/giZw/vcBoRVMbcI4wOna3twybLUlNReJZJmqfKFLqGSmdBqbU+xKYDSE/H6BIqBgzyuUD0ap9bF0o2W6SC4NTSdVNU8+VI9lnhZakmX2hqvPZY04RrXSGEkTwxXuoqirj9KS9sp0w+cOEPniAsv9++v9ECxcP1u0+e/Xo3kpNXo55OiIHjkPnHMOQF8k4drmeUoGtFFWt0Vby+O29rRyT3AOW99M+34BIp3jf3d+FFPExIN5ByolVWiesZMs9lvZO2ecq7DfIlBibEzIoBNrlgVXpBg4Z3lTkV2UKi4eWRG4LW4q9g0diAqWyAlQZCkwoUIVzUWDqOB6LR5BSIqTsZ5t7m6qxOeEopcNaISZiia8HH4mmJIhMTBuSwxMhBdbbHWARHLt+pOtzqYU2jgSsuw2bzY7drifElGvFtGLeZIPFewdGZ07VWYOxmug9MeYs3GIikcgGElCy9jKH6m7bc3O94uZ6fe/dpn1d5qzKhOSnsxpb12ht2I4DfT/Qk7g4Pc0cw2fnDOMIohhGR+UDCUFrgzE2l1cojQQLPmedxyGitcVWFfVsRsSiUmAoXQYySXNgNwzcbrZ4QNmKdqlRpkKULnWlueBaptjyNJeO1u40f3KdoKBtTZotWJ4/Zr27ous3iMpeWEy58uKQE57Ps6eySNnwnqqkYjF0csuvWBK1BC2BbhhZrXcMo8kw/BCJLtLtBqJRpNoyxoiTlLlQJZGMoGpLikIomdtRWaSqc23i3S2pJBOSm+2WnyWWJril1lNKzz5tsrtj4oRKJeo6sdvtiDEnKO3PW3oUTiVUKUHTtFRVjYjQdT1939F1/RHheSpZo7Y0tbbFQ90yjCND37Pdbth2W/qhL8wykKJwdX3Nu8o71wnOljOW50uaxZJBZ8uyWd+iZ3PqdoZG0CmfUJWN/QDXgdhCCVQ6/kbv8H5Adz16t0O/eEF69QX9Zz9g1sxwtuLlasXNuqND8MPAMO4Yxh397paahsSC5cWSk/NTlHN040BU8DO/53fRNg1NZdndrhh2HZvra3Y3tzSvXueuw7mOvmRmBuKU714SSrRorM7sDd45tttcp6KJ1JLd+xAiGsGKptIWK0JSirnV1BIxKYAbs/LfbwwTJJpfWGbcSVRaUKX7swKs5O+MtjgfcC7QdyMgtO0sUxTJpFhiafFz950lCszJFLOarn8/kX1akOoItiwbQnSMcYVXlsCIVjUxwmqzZrtdsd3eEtKIrQzz5ZyT0zlVXSHiMxY+4eECe/aXtI8O3buD6a7KM5GL4+/cp0oEPPhEcDkTL1LR0FDnegj2ZHFpooCDqnixSZeWSSV1X+lJWR8GziYyJGgqZPQQPa2ymY9UCz7k7tuVhSEJISjcCMkqMDlGmERAqVyEj87wUUq5s4rKNVxy/PzFAPM+YBsLCa6urhlWW+LoeTa/oJGK0US6vs9wuRY8Hhc8L69uSwZrLpG4uV3x6vomj2YIbLa3rLuRde9yjaTVWFXz8fPnPH18zhBHdkOHC55Hj07wEtmOPaGULQ0BlMlZqSGMKKVo2xlut2XcOq7cwA9+8Bkvvri6owMFMORu6M/PZpwtWj58ekHTtGhjeb1ecbtac3V1y9c/eJ8njx/z+PFjIlCtVtzcrmhCQrTB2prK2GxAqEJ5NxP6Xc/GO+amQoxhNptT6xoVPbHv6bZbxr5n53a8uLzis5evCUpj2gY1mYnFeJ+aIFMSyGLRTvfsV7QBrXO/RDM7Q9uKi49/lu3YMbgBe3WFLwXeHpObUudJmcMgJCbcYs+2KVPvzhw2yBt6YiSBy3Rzw2djRtREYVJmVwHItWCw2WTDZx0jlfJoDdVygeoCYRfYjSm3yTJzehE6ubu+IDMu2bpGW4t2BsTlDF4taDuxUSiMtWjRaKX3hfGz2Yzr62tubnIi1USN1nU5xLTdbrIRZg1nZ2fUdS5tuL6+zh5d3+0p1CAn2iyXS+bzOSlFPvn0+7x69YrVesWrly8Zhp5hHBi9Y+I0JQldP95BJL5K3tETFE4uTjh9co5uFwydZnQD17dXxOQRHZnbKtPdoHJ2XZoAt5waHyEH6bXCb9a47Ybuk0/g6gpZrYivXxGcJ+mKz88u2LZLXq+3OZiccoamsoqmrXl09nVsbamXDbXWqBgZu46Tx2csTpe89/H7uUu496AUts2F7lVdlbq+IwggZFgplkLuGCPbza5Y+wEdI+PQs7q9ZbNaE0MgDX1hnPBsXcQ2FX2/KSnT4PtAaBRBR7bXoTBKKI5jgnlUBW0sOkXaSjNrK9qmYlZZXIGQREyGmmNAYm7DQrG8DkowvfHC0wRtZkyHDMRNyRvl6vtCty+TWDJrAzF68BEvnhiEcexIYcQqOD85xdaWZlZjKpXTxCd49b66PTIGvvLS5b7va3ZbKWytS5y1QDjeI0OGTzJvZ/ak/OhLw81EqDN81SMF3tLUc4NB0VhdugJQNqZ83k0M+BTYRkdtMjWgaSrCkAkAWpPfRecjxDorIbLH4YmgVU6OSPoATKucIWeqgyeoRPjwyQXL5RzGEe/GHHeSVKjENNvtiIodycF6cwWSWJzMckzbeVabLcPgMqS02rDedLy+XeGHAQU8uViiqgrdjHz64hUaS2NaTirL49mM9uQcR8BL7lDRuwHbR7YuMaaEspGqqTE6ly4ok+HMVd/Tp5HgHbayNO3d+kdNYl5bzmYNHz97xNmy5dn5CU3V5LgagaXRnNY1p4sZtbUkYDabZ087pkLCMLJar6mrmrquMEpDTIzrjhcvL7n84jVnjx9xIpF02h4ylp3H+YgLiZKzR5TiVWpTMpwPqEwiGzBSyh9UmbPunhbU+qAEQaGqlvmj55w8fp++36I/+U7JtM6thVISJOZynVRQM2APl94hxiChIrk+uPwckjBGsKJJksdlnPpRQc7DCIHPXlxypYRVSrTiMVaBNTRDoh4SiwiVbWh7z6Yb2A2HbgspRm5ubxhcj4s+51KUZrdKsqFgbE1Mucn6rhuzwa4PSjDfSsQYizF17mRBoRf0mRtUVM7CbmczBGEYB9yYWb6UgOjcD9A7jxtzDesv/7VfJkXo+4Fd1+HGMXt/4RBmmPaIA0L2bvLOcGi7aJmdzElVSxocbnDE3RZbaZrGUClySYTkeIkWoRKdYRdR+CnNPwbSZo2/uSG8eom6uoTNGr9aMWiLq2dcN3M2xrLqOqL3JdsoUpuWqq55/PQxplJEnT2n5F3eFE6XXLz/jJPzs1yI2e0Ivs0BXe8hRprN7q4STKVoXHJGUwiBbrcjupHoHHjH0Hesb28Jo8vxsr5jGIbcS1HVWGeJcSgIv2BUhe80TgV2cUCUzskZRUlO0KUSRVU1JDdiVN4wFDFboT4UqwbcMDKOOVHHjxY3jGhdUDNdrNS3vvO3WUJvVz1vV0mHGkVJMUf2JBFDTqxRhbJs3jbY2lLVtnieiT2PRJqumfa39NXK7979Hyl3gdy30maeQJ0kx3OJdxgiJtb54Ar7RYqIKGIsfRNFI9qQqoQVjY6UnoE5JpjhKaF3iUBkkJxtKCoSdYb/SYIxYHzK9V1HIx5iwMUMgumkiy4vR0jKNG36yAAReDSf0dYV6902J95ohWkqMJbRKLrdAD7hhpH15hatBdtYXMjUaF0/ZOrAXcfl9RWb7cim6xh3HVYpvvHxM7T3iDWolxFrFOcnc5ZNxbKpOF3MiVaIOtfjWZWQ4HI8nYhNQltrKpM3YaUrxFicrUjB02udO7XU9s7rNkrRVpZHJwsenS44nbcs2obG1milGZqKSgm1sdSVzZ08YsRUFY0IVb0DEZxzdH2f6z2JWK2RCH3Xc3u74fXlDbGypLZiHjw+5RrAWCDJWFZnJJNFKJXjWhluKKjD4RWVQu8CVaaIuufe6kKgHVOhjhShqk6YLR8xP32ErXLJCDEUhr+0n853SPpJR1P8YBzv0y3LakpSEj0l9yVEci3q/pZSQkJgve3pJUOwMx0xXqE2A/UI9ZgYrGLWD9ANdMPIMLqDgUbKe1/yDCG3+qpszlTPZEu5e0yI5L3IjcVAj3eUoHOZNUipw9go0QXtKESb5fgMm457GjzI4aSpzCKfS9hte7z3dLseHyZi7im5SkDpfY13fph38wLhR+gisbg4ZXFxym4Ugu8YuiH3UiOS4ki/aDMD/mzGo2aBrhqqdk5tLFYb3ODwl9eMn32K+/f+MsPr14wvX+GNJhnN9cVzruuWy2bGjQjjOOB227LJJZKGuZlxenrKN37qZxAVWW0v2dzcoK3m67/jp3jv4w+5ePYEP45QNiathGQUxhpsXWOb+k5sLrOmZCW4227puo7PfvAJfsgdr4e+y15YijTWICnmguO+Z3Qj6BY/GHyfuW2mF+63V9yanMY/wX+ZSDhzMOa4QoaUUJakDN36Bq0VV9ay3fV4FwkBOucYQ6BpGmazlvXVvHRtrzh9dIqp9USleeed3Y+y/eokkZdUiRfEQEqKRxcz4miJpSO4DgkVgBJf83fgzclinpJVjm7yRxGBqqmomorgR5TJm1hSZRMoMVhRIIXZh0TJYMtdHlISRGkkRvxmyN0onKZtLJXNDDJYjRhhDOA1DLViNAmlPbu0o6prqrbONZFjwvicMecVBHLM0vvca1CjcqkEUxwlF0wfP7sCnlcKHXrWrz7nw699xPLslNn5Bbfrkdv1wHe/8wld3zMMPSkOzOctSQvD6OlHx+1qSzf0bLuOz169Zrsb2HaJNI6czmf87M/+FOtuy/VqhRs2fPzBM/4jf/D38+FyycwaxA9YMSgMWgxndkaYV3Q2M7LIlAgiwnZIBGUIYjl9/j5jHFn7HdEnXO/2805EOFnMeXrxiK9/8IyzuaW2WYmGmK31RVMxa2pO0SRJODcQB42taiqdzcJx9AyjR5kKMwysths0CULCrxxffP6aTz95xa0bcUZ4/hMfFK8834mqLFYrolFoWxU2n7z+pn1iCtukeIgLp5RLj/ww4If+zlScMoxTUZhKKSoz5+L5R1S14dVn32V99ZLN9Uv6Xb9nScnhlun1v30B7Dk2j2uaC4wqqSijttr//aG/KbjiVe5ihsqVFggWvEPGQLvdMVc3PLKf8eryltW6u3PdzWbNahu5Wd/y/PFFrofWc1Qh827bs2xAima3XhH9SPI5jjdllE7xPyUHmknnctNco02ON4pwfXMDKRXC7sLeo1Qp0o+ljGIghk12HmK6Y+yqiaABuZduUOqn31HeOSZoKou2ljS4nAk4DGhl8GPPbpvxau9rlAZbWYLRGCPExhCMxXY9tutQl1f4usU8fgqPnrIOgW2MXCZhlWBNYvAOHyfy1CnNPTO1zJdzjDX4MDL2A1Vb0yxbzh5fYCuLHx1D3xPGETcMuGHAj/mefWFHv6saClSYIuPY0203bG5v8iYWPP0wZq+DTCMkJIJzuRNyCBAGUvIobIa/JHcO6IKjVyXhrFiApmRIxRjxLtcsbewGZWqUqTInpmRy4V03KcEc5A4kxrpm2Fj6mypDT/MWFCzPlr8qZffD2z4djxGQYlFjpfOCVmhtc21gyG2DTGMzkwyKtLdUj7JOmDbIL7/+/fjLsSitckx3ymyFHK9AioUte4tQlXIPpQ1iEqJSCcnkSFDW05EYBedylrPWep/5G1MiKYU0FbSZ59JpyHmmirHEdggqV9abvHsqLXs6wD2LzLRAVcpf97hgZ9Yyq1rC02c8PT1ntlgymy2Iw45OjXz+xQtW2w27vqeywkk/5/zRI3Zdz64fePH6ugyQ4GPKnJ8h5BizhrbR1PWc5dwyq4XnTy54/9kZs5QwlPi0JFSKmTkpFQ9XZ0tCaQoxhOCVYgRiDFRaMvTVzHn2+Jx+1zM1CxagrqoMn6pDLCkiuXQo+szwJIqkc386Ui4L0tYikjtQhDixjhyyrnfeZeIB2zJ7cs4T0czOZsxPlvgQGAs8Tiq1bMFzs7rl+nbFZrsr+S4qR8clKyjFgSllz3k8utyl3o1HJl3ua5nh0Nz9IteqJprZCZICT9/7OkYpkuvK/uP3kOediHzKq0sm5698NvXhS1OaalkUoYRF8nvVe+9rUoRa6dwUOhaTs5RhqLpGbIXXgS4lbtdrXBTiHQ5bwehMp+cKTKpVNkhE5ZwO50LJ9DkYElEyBVruDHHYZytb7zOxu65jHA9jmFJiu91CSTp0Lv+NpAPR9nRcHou7Cr98WNb74djp634fw6+Sd4ZDtbW523Uc8qQYR3TV4IeBLrjCDedQlULXNd5qrIoEI4RKUwWP7jvMzS2yPKF53KIfPWa92rDddlytbtj0Hdtul63BkJk2JlRAos7JF4sFSmuiS/T9kOMAj885vXiEFmHoe4ZdmXjjgOv6orTzPQfnuTsHJ48l4seRvtux3az3dS79mBeSIuE1BX7N9D+ZGX0gJZMnZVXlurFSe5hSxFq1d+2tsSXpL9F1XVaEJEzdoG3DWGAEEU3fDwSfk2+SykkWzhg6UawRjLXMlnOqWUs9q/nV+nxf1RtxPz772JwgMmW5JpTkNlndOBBTXgRzYzLMJDm2NqXlTJM4/3QMAd2/9lffr+jCRSmaKa6pdYY/lUihd8q3nFHoHHcVnSbu7NwgOBxwqYnBIqVEVReYZRqbUtaTFjVYTVQRHzUkRe9KNl+IYITi8GUOUVJhlMmcpKHgwEmlCeC688ytqTlfzJk1M5anJ9RNS9vM2VUOK8LnL15wtVqxGXqWbU0/jjx+fMN6s2O76/j0i5c0bct8uSCUZIuYIkaRWYFqxaypqcySj98/5+xkwbOLBWGzITlPjOXdkp9HJcnUW9loR0nYw4pWqVzsHxNWC2KzJ/384ox+2x+8fRFsVWFKnC8VKDImIbi8Z4gf8wabEp7czzE6Sx1iacNkSunTofNAjIFt1xGBs7MF86cXNGfnNLVhNst0f13XZ6VJQQK859XlFdc3t6y3232zbeJUzFzyNwva4Yae4D3O5QbG3t8lBjdaMCYjHBPkh4K6XWKN4en7X4PoGDZXbNerXDTuOUB2d+Z/2nuU07rYb+JK7Y+XomxQuXer0lISDfNLEpEcK4WDEpTMy6ptLhUZ08iQhLjdEFGgDzFcASpjiNETj3qEWlsXGBmGwcHEibv/K0rnk6wAJ8ozpXSuu3aOrusy0YgxeaRTYrvbFu82x/9C8MWYOFJ4Ze+c6hCnTvd3xm56dxTatl8XTxAQZVDGYqoqlzhEh+vWOYXcGFJ0xOAQEn4IbOoN21VH27S0Vc16FKr5kvp3/x7Ov/ET2GbGZkhs/8avcP3JJ2xevaAfevzYE1xma0mF5gsRarugaRfMlhfsBkWSirP3nvLhT32YPaEx49thdPTdDj+64gmOBOcYS1+2ze3tnUE0OsMaKMXZ+QnGCK9eNNzeZuUcU6Z6kpgyvyg5aJ2L1BQxwphymnYfMn4twh7+sEkx9UqLMQ93SonOjTgX8D6ixgGtpoajqTCKlC7myJ6qbBz7QymKNSQVGF1PiB5dskB/bWRSXJR/C67IwVp1zhGHQOgVm22+d9H8/9n7kxhJtixNE/vuJCIqqmqDj2+KOSMzq7Iqa2h0s1FoNEBwTRAgwBVXTaAXXLA3veCCO+4IAmRzQTTADXcNLgjuCHDBBQGCQ6GrqokaMyuHiBfxXrzng7lNqioid+TiXBFVM/f3wqMqq0ACJRH+3NxMTQeRK/ec85///D+6U7TagtUwE2TUTAhPJ8/+8dp+Dw6FJAWo6uitUFZMNI0Stf5cRF/SV9Ufo+yiXqOMJqWCiVkEv43CtarORQqFuzUK28gAhTaGpl+RWkdpDWYlEPDkEzeVlRtNJtlMMbXYyTIacZqlxxyFhJHklo2PGveq6XH9Oa0VItQYwUyTzO4BWYmNVUiZ/ejJ3PPnf/krDtPENAVu7vecYXA9rFYbmmbNui90KvH8vOfFRc8nT895cr5mDF72mbDHEMhKdHVLjqAMBlF7ytTzkjMqx0UXMrk1CZl1Mz7XfnThSd9xuNgeC39gCJH7ceR6Z1C5ITYWuqYiLUkYu6WQQkTrhLUNXeMAETxvnSEnTYqiDSkbPmjnoIit02rV0p21HHY77nb3XN+9rn1g6WmN08RhHPmLX/6Sm92OMfpaCVaSlNLkIgS4MI3y97hfdEW1sbUiPh6uukgc538VaIVWDUYbXnz+M5F4O9xyc/OOlAT2qwvipOd3/Na8Vpb9ac49T6a6cs7olGsSnii1Gptn5EyuI1FzEFWKPCaKa8A5itZkBSEXmu0TXGkefK7GWAKKEgI3Nze15aPZ7w+1khPVF60N202PppCCx3u/QKKzRdW63zzQxph9ClWQhMFoGW63tvr/JU40lo9nR6DQXCeHjgTDOWMouVTU6V+t1fLRQbBUT66mE3ZW01iCD1VcWOS4lILJSpYcfaTEQuoCsWlJxdEoS9d2aNeCcdynkUOMTLUxmmKQ2aSUmPk+RYb25MRbh7YtKIux0HcburbDGUscPXGSP6HSccM0EcaJGAJ+GJiGAT+OD4OgNbjGgLJ0XUtKkVW/YhgmtJmk4VTmDK6OtlaRWpSqGb2aE8iT0lwWrspHeG4OUyUXUlakyhosWVVt0LIYT86efEqJzNbCChVmDc4Jvb/rmtpI/tgQ+BCG++0PrUFwKcmp50ODMqBELrsocW1I2RDSDMVotMhmyHk58QlcnKp/63t89JNamSgz08oRaFEDVYpLFwW6oH3NkDVL9WWNqdWsMN60YXEFKaVuTlX3LFOqcfii78U8dZlVIWpFMYpkjiSFGRY7wp318Qh9O+VMUnkRg54fo7RCGY0y4qxOpqrMSLB2bYttHGr0ws6Lmf0wLuSGmAo+JMZR5ietsWzPetY28/S8Y91Z1q1l0whcNjtpKERvU83NMDVLXKgarNODylgEkSMlKVESU/VcaakerTox9aqQ7OQD+2Gk1UKMs0ZcAigF1zT1lIu2sEryyrMWrDOGoPXRB9LU0SVliEk0R4MfKWFkv7+XZEwp2rbDWoN1ljKJ3+B+HJlCOKIbWZScUhR2q59Ggp8WeL/UgFKUegiv1QLM6EqFmysXXZ0TlKJbn7PaXtCfXWJcI3RS4rESVCd/TpGpeWM/vtTyhQQBdUwySv3PES1l6b0smEsFPMo8FyyD+8Uo2u0ZXbFLUJ6ThuAtRmlp+4RATAEfJ0YvbEyBQRWqeHFwz0f4cR5uV7V9NVdyMF/jhHZmGQ+Zf28m1sQYJehzPKdaq1pUHKvC4812coLmr9TxmnzM8dFBMGVAW9bblrPLDePoef31W+kNWQO1hM4pEwYpe6Mb8f3AoV3xBotre/rtJbe3OzQDd+/uuL56w/7+RvzCqhFuThFh0YlALUUgLW0atGmxrsWtWtZPOhpjUSHh7w/42v8bh4HoPdM0EkeBQ/1BguC42z84b13nWK1a0AL3agPnl+d4HxgnUcoXJEu4ZXLzqEqoOcWiWcrwOQvVWotwPnVNhqrRmQspaVCVak6uLvay6Unf8mRWLubqk8Uy+NudrelWLZdPzmlaxzR43jsexJIPLYry3lfq5L9Li6Lo4yPrF7YxqOIgO5STus61hpgNycud7ZzGOek1ycxg/QziW3Vy/5cH7/B7w3mNyaZWELOzRtGidTo/L6VgoyEneW+5JErKdK5DF4Wexa9riBK2plko8TkmYQtqwOjqpABljIs0WrQC90RXQAXm7XuxX1Jz8BB5vpSqBRmJmPKDs69MoWjxHwxZoLcQRJzdOMXm/Iy9j+wPHnIi5cJ+nBhGjw+RjBYx6LsdjTacbxs+ff6SJz08O2+5WDVsLHRFtF5DdVnR9Zza5cxnUh2NCUCo86yqaFmjORP9SIyKEmWFKlMRG6MocXpwuUbv2Q+jzMymwNg2FCVVu9WGtVuhKjli9IlUAptYcK2WEQZriFoxVWHspm3p+y3NOOJ94HDYcX9zw7DfcRhFdPns7Izt5kmdRbSMPpAzDJMnRpFuUyVRUiSNE+Mo/ar9sKs+oqJotch+hRkOPa5TY2TuNFexoWX8qZZtq+0l28vnnD//BNv1qP0elJ9XyFE0uT5hqetO/noI94EkndoY6cNXyFPNAZCadFJBl5qUz8IgGunbZcCVIsPujeH85UvMalzuQ6UU61VPSYnONmL0HDw+enyc8HEQomCR/Wp/J3PMXduLPJ7WWGuXHnDXdcseqWsiMY4jjganGuFSaFVd7GUm23tfYVyxZzPFgFVLu2JudTyOcY+D3r+RIJizgqKwjePiYgMx4vcHGROIiegnsV2JiWS9GLKuM0OMjHaAYjBuh9/t8fs9aMuwHzncvyNM9yQ/SCVY6o2vDe2qxUTZgJXRxOg5HO5R3YB1Fp1b0jBRgHF3INQZq2G/J0wTfhiINSD6YZTv7fYP4Ib9bs/u7g5su2Tqz549Y7Xqefb8GSmbWp1JcC7z11Ho92EU1+4U5sFQaazPGRqIarrAdDLQnlVlrdbvlVw1WWIUibmSWUx0q0TWAkoqUf08vzzj8skF602PspppwU7mj1YVTJbfhMU19D248/j3wuSfIR5Vrz0y9jBLkVGc+Ndpi+s3KKVxjcxdpRS5v7+DklAqs+oVroW2sxhbK2Ml5JTZZ1GdvKuTnI7yAUuUY99AznEii4sASH9JCXnGdQ6ySPnFVLVS6zhNvTLy2loINyglYxfAGCNZUBuRikNmNPUobgegUEZRqiVQTW8AubbKGShCsik5y/tCIZJ0EB/1LFrrac1YK0Q5z61zuCSyU+fnZxxGUQzxw0DJAmmlUtOlLFBrTvJaYfK8+fYVF188odU9K2sxuZAnj84FU1VgSIUSM2r0osdowOdEquLoOWtBQ5Tof4pTesZl0HkmkyQoE6oY9Al7ryDJs4+Z3RTIFAnkMdPahtY5VNeTsmh67kePNhMhG56lC9ZdR8kBowptY5bN1xmN7VcEZ9ndX7M/7Lm+vQUNtm2wjcMaIwYVMRCmUbROYyTXqm887GVfOBwIlYiXlxYEFeWoqAAnRXu9E/bpHSaxrF+oLY9K/FEkQhNxZ2vcaoV1LROHRW1FVe6A7BMPA6JCzVyd2gcT14q2dTSNaHhmayhWk+dRKVXQSO9Z1bWYi1R+aa5UEWTNtQ39syf0z88JRi83nAL6vmfVtuQUeLe7IalCjKBVg7VKZknjRPAejcJZaTf4ugaHYRDvRGu5ub6RUbVpEuegJL1EZ0V+0zXNUqXO1aXW8/yy7BG5KrLPakrS85tX1+meUPevInOlj++v7zs+OgiebqbOatrWLguTUojVGaLESCrIxuMdKWZQAaUMOlS4MxfQhmkM+PFADL7Cf7L5G6OxTjzrGCUwSXtFRH0lCDWobImTRxWZnwpexhrCKAEvDAPhMEhZP45Cjhn9yYouhOBFoinJZ8xFrG4KiHRUVidBUALU7DpfUiaN8rUEwbTAmbNDc4q5OmjMGqrVtSBJpUAqJDXDFXUb1XURzNDGnDXmLMQLZeg3a/qNmHWKcPB3XfTTIFf98eZwc8LW/C5ktKAE3mMeuJcwJfFc6K+2qkjIyIJYMfkpV1gpEJKh9ZqUjQRCowQdmqGdD8ijPQzUD99RnqGsmQlWsvTcdK4EGCNCDaay47J8bpGxSkvWPL/CDMyq6hmTkACTNQssZ9BVVlSG6utpqYFXZslK/ThGK/FvLPLaud7U8+eZpfpOL5E1kcYEopbrI6LVBecUbWNYr1pW1dw51B5WypLVW+uIIcrsmtHVADpzd39HjGeIwqWhJAglQZ3V0sj7KInqPZOqsolUfDJjqWVkQMlakGSg9sVrWq5UqgmPQj1i5eUsIu1TBHQk5Ewoiq6BNkE3yr0/+Yn9OKGUIWNoGoci01lV51Flq9KIBJugRLJJTsGzH0eMM7SpQrlzYpUT5CjOE0WqvOAnQYn8hJ/q7GHdx5Y2hzq5bx61GkopDPEeGx9un1opkqqjGQo8E1hQxtTkbF7vC7b5YJUvXx/zz9pmkt61dYa2ddLXnqOjrkxRJVJ9c8tEYFlZgiJVKeQxRUFZi+1XqNaAOw3ACmcdtm24vLhkSBNTjOQM1jYoZYl2IoUoMn4ARRFsFDH9Kos2739T8YtjRKg8D63kvcj7kbsvp5pUVvTscZk3k4bkMUfGuTq5NALB6gqFiwzexx4fHQStbVDa4sdAGAfSdMCowLozbFctIQnEMwYRS865iPLK/OFsUz3BJsxhoBQlvYL9jmkaK9wqFN2+X9GtOi6eXPLq9WsOw4BVhZXVbFvHIdyhp0gcNaGKCIdpOlaC9/fEYSQeBvwwkEKUIOg9fn94ADekkIlTJOmqvVIKY50RDNGTcqzvX+Nau5T8s6lmr8VE2GgZcC6l4H1gv5dZwvu7O5FmS4UYff3ZvlKGMyEWSioS35SwTMHVdSA9jxIjJSbCOGKdo1v3PP/0E548f14rjffZUDNE8hBo/HCkex+GfLQImYNsrpWZJmWF0pam7UhGrndOmaZpMcqSs+H2znN3d4e1STbzleb5iwv6dcdmK5mlNgqpbWcp4fkdyOs9zvhiDHhvBH6rFkWzgXIpoJxBW4OxjlXfYuqoDlqjkjyrSNVJHzYXqVY0ouhhlEBHUQm0ZKg6sDVAllpNK8BUtiJKKslc97ZUGZAURaQOW1O/l8RpJfoT9X4KXXtgvRa2bYqVDJI9l5sVq3XLJ+cdh7uW143jYK2QDGLk7OyMtmm4u75ms+44325Y9T3DYeCrX/+Gn37xhDEmYrTcR+m5tX0rpKCaPCZfSEEJI1BForbErAjZLAIJ1go8nxFxeV2ttGKQ5FUIYwUdyoNLFlJkDJGYNQGD0QUdA2ZKWD1x5z2qaqD6IAxU9e6GwQ88vdjw00+e46yVXlUWvhUlUJKMVIzRc+8919NEOCQmNP1my/PzSGs0xInOwnZlaXTmfjpwf33NWDfrHI/XU9cAVSoCUer4DDy0Aisl82b3aw72rlZrqhLW8gwmC4P9MJH8oarGyElJWR6hq/3JSW4kCVO9/Y5pmjy/cYKMnV+e4ZwkOdEHUoyoIqIEVhtKiksilqpRcbEycqKtZdAF1TWkruHW33EzPJS5s9aw6jq01uzDKIz9EDk7u6BxDTem4Sq/5f7uAJQK8Q9LH3Uch2OPtLAUBTHIerfO1f1a2PpKyT1HAVWl2ObkNsZZ95WT/U0srGa26MyAXW/PaduW1arn/Oyc+/2OP/3FL7+/tTJ/5o94jLx0hZlSEDVzCiSt8VMg+oGVa3DG0K5XKO1kMVVUMFdSjaokBWMUIFS6HAO6JEwRnyptoO0cxllCCozTyDiOrGIgTgPTcEfRwk3PMZImT64BQnqCnrA/kKaJNI6kyYuT+BSEdeofUp11ZYctVVEBpSRz08YuAWAONqrCRArJfKNK1TxTV2mhgp9nZVTB9SuaZVELlbjdrdjt9pIhxSjaoFHMKalZzwwVpiQBPOJRLlGsJWvF7W6Hbltcv8I2lpweVlLiuJ0WOEcy2jpbVzjCrfMyKRXorOXMDHse1S0qElBkGNcogzUWqy2+iLSaj4m2sVCcBMMqaxNzIfnElBJt2xJ8xA+GbmVpWs16YyuhKKGqMeZ8ftVj7dCYyCERc5Q241wV5tnJBIgFbSQ4WWtoWicVzgnUgqoVz7LxychFKYqUq1pIyhXmVGRkc0kpUj3WaSpN3FTorLAAI7WPJn9MUZgZqgxJyB8nfoIFiOFAipambTGuBSssVhqFLZrewcoWWpsxKhNrhdA1jn7VkYeOlbM4DSlMpOixBtrG0jauJltRNHtLlorRKGJIpFgIqVbOShGSIuTCFBG4Vwk7dRZ6Fw/GQooZVYnys51SyQ/h64IkljElQQrqppVyIZfIbjh6QsY8k8MKr29uGaYRR2G9WrFZrWhcA6kQDomMGGLHGEilkLUhFcUhJt7e3vF0syF2DU0JNLpwvmp4er7lcNiTorAclRb5NKm8j8meQipYhUbpmpzFykydg1mKhOSZndNLShBGYvIL4pOHiXQ/EOKIstBuxahXG31yX9W+8SNyidZakJ8iM7m2a2VetW0wbUu/agjjSIoBjYxGiNbxUXVFbL40umlRzqGsEzJP11IaK16qJ/A1yPXW1mBLg9FWVHkOezbrHt02GKNp24Z+vSF4T06Jycfa/ZF9PtcWjlXHOUYBtaRgSUHaZrGyZSUBMZKYze2SJYWXc55qtai1om1amrZlu93Wallzdn5O1/VsNhs2my1vrt7xoFT8nuPjRySUsHlkNq4GN60YY2C/H3BrhdWavmnQzkrAjKUudihFV5xXshqlLLYYSB5DxKkg1b2Vn2dg8uJIPE6TQJrTgelwh9k4QEOMJC8jEGEQenPwnjgMZB8kQNZsKYVArif/ND1QFSw6rX5kMNSgtTRrc8XXF9ZxpcaXUvAIVX92JyhFZpJUXcyuaxbHcmvFnFe3Dpxhqqy1EGRY1E6+BmFde4vVsDLLxqCK2JwUrbk/HDBty2a8YKXU+3MxKh8DIaUGQFdJKZLdUqutGZFcAt+yNOev5r6FQFCq0v+tMThjyDHVgijROoNShqZpMMZW2SkRcU7es69edtO+0G8a+rVlvd5IECxVeL1WwQsZ6eQoMz08VoPkUoWP0wyNJlCZpAXGs84KZDjLOp3CLQ/+rjNs9VyUrMgxL/BYlAauqP1UeDDV522MFpcIWJwDcpndVEQf0mTIqaBTFtPpU1PdAiGO5NSi0DjjUKqQDCgHpkDvoG+gbxRWF5IWmK1rGumdrTraxtI5wxjEcLZ1mlXb0DaNiH77JNVDjjhjKI0lRIE9Y5aZMI0mFoVPhSkWaY1pVRNf2bkLogGZo9gxyTYla+OhBqYsoQyQ80LEkSubRfGlGr7qijFLZZ65ud9zOAw0qvDkfEvJmcutVNmTTyQUY0r44EnVZSVrwxQz17s97+5uyb5hY7I4yljDtl/RNTVQ1GsunnWy5ubEs1Ah7yK4gVaQzUPT6pRTFYhI+KkS+oYdIQzEKFZCeZjI+5GUPdpp2vMNXdcJCaQc4fw56MqcXIWqjV6Y96VkTONQXQtti1619OdbQueIwWOU6No2RnxIoVSzZ1nvpl3JSIm1FGMIxhCcGHjnDwQJpbWQBCufQdC0ILCyKmJ+3PcMKELw+BgXU3WlZWyk1PtK1+dTlipKEWpwh3GaFqKftRbrLL1tF9hYzWIGZZ6b1BIE247NZsOL5y+W+/f8/ILVqufs7JzVak1I73+u7zo+OgjGKFnXsTckL+JT4d5nDuMtjTVsunu2TUvjLG3T4pyldZb1qsW5lrbrabsNWltSgtvbwm6vudsXfApMKXBzv2OYPLd39xwOB2JKvL26wqxaTKv4o3//b7NatSgK0Y+EYWS630slOE3EvVy0FDzBy8xKqoPyORxZXiDElHkhLJ2oeREojdGyYGWEYc5sWZ5jNrWdF1QpIF4PtfOkNVlLn8nnLAHVWbKzAh9Mk7i0q4JysuhCSoSqvg4K3Ta0jaXJK6EMWw1G10yushV5CEOJ+3N1l0Ady5Llcen4vXojko/0bVUJKXKT1k1iHh9Ag/LoqlKva7UwZ9S6Ks4rMw+120VEPEaLKpqYI9O4Y79TPHmyQTsRWi+VhCP+bbW3dnLok7RF13/PmWSprE4UKAPj/QTaM42J1aqhaRysmjp3dyyGC5U9WmS8ItceUfQBVRRWaaIP5JSZ5k1baZxWNM6yaltaZZa+2dyLiSktY5I2134TiqIMSZ32LArDfmAcGrrOkvUBrSzG1LEHr/j0WY9zL7k4X6P/P3/O3SHQ9FtePH3Cdt2jztZcXpzz7Okl//RP/4RpGvnRswt++sULPnl2TowTqd7DKUWCVsRgyFESrpgS1hqR/FOGqQT2PmIajUWjjBMoL2emSfo/ORfOVgarNUZDLFJRnm4/pw7sIKSaJCDxksVDHWurbBCjBe4eQuJX377j6m7Pq/6Wl5dnIsoTJqacGVPm7WFiFyr5XyliStzcj/yT22sciV4lqSKt4+r2nmEcUdbUpa9QufamlIC985jSMT8SONyYo6muBMtIyaHS/j0xSv97nKTfeDgMECL4SPviCb22uK6na1usrQa1coNVC6BHWpiVmarm96NlFla1LcFZbtctqJWgE0qRlCZozcyXjmIWBdrg2tUidqKxdNZK4oRmuN6f5v/EnIXoGKMo7YTIcDjw+vU33NzKzHQpsOoEgdDa0DQtPoxM48g3334t8HgpTIehzkIH2la0Yn2WllCM6VFfXDCXYRCruKPOsnAAlNJoLYLen3/+OU+ePOHy8pJxmvB+ElnMxtF1ndxR/yYUY4L3tfyNsnyNTHqIEK3Ch1nhpRBjobGGNiQaZ2msOGM750UZIIiaQE6FcdgzTQPjNDFGzxA8+2li8p6QItoYnDW0XUPbiUBv24idSpzGRb0m+BoA63B8DrFWfnEhssyL7fSYe12nK6FUDzxqU5kim+1CKi1zma5O9P3mBauxbgYQRa1BGsEVgtMabS2uaRY1dlHHqRJxWUp/G6qdSAGofl4lV0sTQ9evxLJo9sJ7L/GplamaaSBzqJYZNlU/u8A/84b0uCfHSYY8147zi9U+HAJtpVykP1tfc2ZxKVVhsiJQqveZpMGUjEnyXlLSog6i7bIxqJLquX2Ygeu6GRhjULksc/gSCBUzBl8Q+FEK34CpeJdxYoYr5JX5k5SlsleZ4zxfkCCW0AQvg74hzfqwiqDETb1kYekZrRdvwqKKwJ41z0g12Soz3PyY0q0toImpkPKEUlH6YCERAnSN5slZT9uuePtux93eU+yK55eXbPoVynuePX3CixfPuLu7IviJddfy4skZ21Uj1U/1p1OUJWkRuy0lyv2qDsiDXM+cyUGgrsY1IsFTFFOUmb6cM6tO6DLWWEoMogn6eCWekEwWyFEdZ0RLhcqWPo80YSlFE3Jm8InMhNZ7TMngB3wphAL7VJiyBNFU+8MlRXKY0DkxlMh+kHm2u8OBwzgiTNcZvlPLvS6OHZVUUo7vUd7TEeZVcBTgRtE42bST1mCsVF5NL0EwJoyS4XLTioScqZyCUjf4B0cpyz2g61ubq0VUHRWzhuzcCdFGV9F3fXSOQYQPlNKUpqUYS65G0Ggz4z8PAuD8m7nI+m+7js32jKf+KSF5KTJiXAJ141q0q4pNQZ5IWMqJkjPj6KvqjkjGKcWiISpjYHPyXJnyiHIMFQ1LqVTkMWOMBNvz83MuLi44Pz+naRrRdo26jmUoYgwopDj42OOjgmApMA0j42GQtFYJFGO1oqlM0XEYCSkzxsi9jzVTnmitpjWaQ7ujsYbGOrp2VTHvxD4khpi5HSYOwbOfRkKaN9hC04ns0sWTMy4uz7i4OGPVNEBhmIbK+hzx40gcJQjmscquhVDnDlOVOsuViXR60WfDSE7gsRlKnFUKZAMtcxqkTxaQMrLpzc+XS8185ZibuDM2XooYmnZ9v2RnM5vqFCKZ/318j3X+sAaAvu8lu3JGAuGjKDgrYahStbwUQBJlBSUwz/x+y8zyO/39umjls0lwy8Usr5NzxCIZeExCg/cxkYtGI/qDsh9qcpml4wzDmNAkrBJPtqwUPoourTGz5VBegl/mYc9C18/vtD7OXaZKsUaJskllEZeU6/iA9JBTlEFda42IQc/QHiwu7aRZsGCe+QRywntZRzHm5bVKAWcywUNqwBqLa0wdlC+QE7rImc41aKRSZ6YeBEGFcx3KNIRY8EHIBeu+YZpg8oXOwqZf0/ZnaGW53Xl2Ezw5P5Osfhx58fwZn3zyEuKenCOXZxvWbYPTmjCMKArWVBhQK7RVQgrNog2clSYVmQ0MWa6nShmXC/1qVQN1YYxSLaSU6WMSrU9r8ZUB+KFDnwQRWZ/HgeZUE9S5hyxoglT2RWmGBMMQGaY7SJE07WUT1xqaToI2EIKv1zASap8uhWnZA/w4EFKW4AAL6jGntPN9m2c4DyR5ojx6/0eD36LBuWZJpfua0Ap3Qv7kksUWyRyHwo2ZR69EHk7VBDnFuJxDozW2MsNLRRH0Mmoz8xhqC0NVs1std48mSQKuNChpSySlBKNH5u4EcX18vY7iDtvzM/pNz+Ziza+++pKrd1fc392BkvOx7jfknHBO9INj8KQgEHWsFkipBrwQQk2Iy9JSM1XybZaLS6mQc8R7IVfOSbTWGts4Nps1L1++5NmzZ5yfnRNCqJwOYYRqrRmGA1pbvJ/42OOjK8Gb63ucdeK7pqVy2mxXJAoxRw57TfAzo0fkw6KOTEGy5rtBLfBV4yo7TRXGGPExM/hQOYKFfrvB1KHLxhqcM1ycn3HWr3BK8e4335JTYNjdySygD0z7A9nXCnDytW8kpXiunoFzP+n0EGqvMLrEpqRCnB/CyivWLkctI9Rxpqc+aGGZKWQhn97wpX7fWVcpy6JtKM30mSoM82AoJ32KXPLCTm1b8XWTngY8rHBVJSrMPRqJZllnlJIe2ao7IyWFn2o2WmFMie1z5VghmfkDRlOrGXl8KCvIhn3wxKzBrsi2IzuH63rOLsG2PcNwqNciH7PrUmhX4qHYbz/HzZ5+xgAZpQNKZ2x3f3JyK+RcvzbWSiCzEtSlcM41O4zV9UBLj1rLa6YpCMUfy5xqzwmQ0gZdSj3HUl0I67XglK7kI78QcXKQm7tMCTpxwW5Vu1TcM62KUh+f6yROSkzx4Trce7g7gBoTPkjfcfAjOWlihMNhYlUM61Xh9374KT7CzW4SAWulsJueronk/RVPe4NWlot1Q9vKOtuVRIlF1IasA1XIJLTJspE3VhKXlMRfMwcgyUyXU1gdOYwDw+S5390sbYNpKqhssEkv1kEPcfljwJsrPZEGPEp+nSaJcikqarKgLPJEqd5c2a3wFa4Lu8q+LAlyPiICFeXIWpOMcAyysYAQi0ToZkZFHt7rC01/SQ7fVyBRyqCwJ/eIiIZom7Elk0xceqg+TFIhKXFRmN3ZQbBjQYtUZSCDrkmwyCUKPDwrNOlK4tOZhRimqe81JRQaPSM68YjdzL3wmPWyp2BFa/cI/yiatmPVr2lSW9sChU3eklKma1f85eHPqsRgIVaN5zB5DoeD2MuNnmEcGIdJVMNqRS2KL7KPzIFcEgmDdY51v8Y5IYbNRcc0TcxC3efnF5xtz/j008/p+75qAgs8WhC4RQQxSoXY39+/v+v46CA4TplhqB5yjYgRy2Zs6buq95epN1BZKqyqz0CoKJoqYKtuowYZrcgVZqpQn3WWpmlEns1onDV0zmIVlBg53N6SUmQ67BZR7Dh5SpiH2IUAk6P48pUqjySjBPnh+akV1hHme3ioky8WzsYHD/n9GbZgZjidbAAC28ncmNEaSsFZi1a5ziOWI1Q2V4EVMpn/txBSagA8wkqPK0GHVk2dd6objMo1W7QY3cvNhIhkQanuDKefWlheopomlWBMIg5grWIKMmc5TFNlb4kqh02it9g07ZJLpEpmmYW1Sym0rZGBdi0CvTklGbhWAisqnVHKHSv0+t6WoKUWX+36fYVJM1NTbIzm/u4CcUvzVv6uRNlT6EvPmfeMsdYZSV0URRVMMkLCEVkA6QnlQrRJSAg5U0p6WJkvQVCy4JyESTs/pABjKBy8DDeHqOrrRiiGkhUx5mXD2vadjBRVdxMDNKpKi+WRziis0aycwhn5PKa6QagMbaNJRHkPtRzSWqFSgTIPjAvD0BmFNQWjE4pAyV76YSAkKdWI+4SSJI1HlcXpPVBPhZzdusaP++8xSB7VV9TJpVcVOhTJhpDFHcJ7L0l3nncaRD1IK44M6HqdT8YS5PYri4DFnKygHga8eX08bqOIMpBGqWPiiqrjFcyuIfWenz9XkfOha+alawW3lJFaApq815pc19dVVLcKTE20izCuCguUP4sJzKtzhqhMhUjVnDAvz/iB/axWlAZbZ7MLBcd2c0YMAWcbiIVUZR1zHYEZBvFYDT4QfayuGfWV1FzxAVSEClUhzo71ul9c5rvVCiEYFsbxQKkJxtnZGev1hr7vFx9CqMhQRQ3mNkgp77e9vu/46CB48I7d1IqjcgxYXdiu9NJ0jj6y249c5yzyR2m+snLCE/PiFkbRcY8oy4dxTUO7aulXHU3j6JyjUdW7K0bKMDKhONSh+xiq3mhKFB8gJUo+DqqXlEVup24+KWUJjCef62SdMQexB3nESWx83Bd4+Anm754IO6uHGwAcA6kMQ9d5NZ0fNMdPj6UKVMfzpI3GGYsxBqM0Rr0vR90057TdlkJgZr+mEhfWq2G7BEGyB5WxblZlyMubLbPodVakCPf3Az4EttuO4bDjsLtmHKVP4JxjGCeckwa1cwZoaJbZHwC99FFzlhmxYZygKs1oxKh3s22gFFF+ODknRc+lNvJZlBJlkHnCMoubtsu2ZtByRWX7lE3DFF1HQY6qNbPgtdOaUlIdqpc1K/OjknqbzqFyqQ6nmRwq4pAjJIWPQsTKJZNUFdJGSY8xJvHVDJHhMC3rppTCu3uP0g1Gt6RsUWSGScYxNBqKFfWWrHBKAlPvIq0xNEazrjZifvJkW3AGelNARXIRxwOtDboo+t4xxYkxDHWzliAm83cBXaDRUFpD66CxmdYEvA3EJtGvFD4KO3K7uWTdODZWsbu9R+WHI0hz/1YrvZihKkBbc/SDq/uCNvrBvaLUXNXVebMqwByiJF4xCvmNFFAlSniZN/FZKEHB3M0uykDtBS+dj6Ikac5H8YFTdGXuccXTyl0JEcuoozfk3GNNWSzYVP2suRRUjiIiEFKVi6rrVmzUa89M8IM5sTVoSeBLlvEU41DWVJUjGUmqNDt5TJ5JcCybU8kFigzAG91UybWZWlYJbcs5AgrEKPPestfM70Vxef4MZzo26y85HHaM00Hk2HJiGicO+524vu9HSXazQmu7XE+zXNvZekrTNA0XF5d89tlnfP75Z/R9T9f1CzN+GPfLtTOmEhWNWdpH1PtSpNekfTKvs5QersPvOz5eMUY3oLuqQh6JRO6HIrBbgrZdgxIx1vv7kclHEas92cOWrI+TRThjul2La5xUf1rhcsbGICoYWaNMIntPKCwEnVw9/0rKwvrMWYJgPqq0pHh0jM9VMeQ0bAluH6tU2ZzBnQx7PZgfPFkwyxO8HxAlF1RzWjp/0AUeEQNYeX6dpdqapY4ePoucLClaFFWWUHzeclo28KwVJT5sBMfYEWMPJtQkVwhLEqALgYkUC9OUiWkEnUAf+4PyLmboUxwDUlDksCP7QJwCh/uB66sD9/cjFBmEPT87o2lb9sYIwyxKf3hWSYkhL4SaUgLGFNbrQtMYrFOQIzlpvG9QFFLYP/hcKgujTzLrI+lnPm1WV4hKl6VfIFBpOcbSolAJopf3ElMkliTVnrGSYdfLlQsymB0iKecFflJUi6asyKZeryJISAoigB61ODIUpRdIPowSBKN/yFIOoYiiTrPCZAnEKGhsQ2MdBpGb8t4zDQeMhr4pOJNrtabQjcUYyDrUiiJCNZd1xggSUKT2V4iKf+OqzJw2NK5Da4eKhTEE8uQxJNH4XMlzrzqLMoVp8ngfeX7Zs2obGqWJMeC6ZrkiCrDWySxeqQxeVTBaHD0EDYG5dSD+eJI0zo4qpSpJSRD0ddzJY4oYRokbkqbkWZFF1ft4njIT+UAZyhZ4tMBC4xd40dRkJzGTYpZkVAlpSJuHaaY1GlfHleRxkmYp7dBoEUzI0hOcB+OLAWPdMjI17ycmHZPPuX2CFlm1XIRVbLTFGiuzeyCM8lnjNQa0kr7jHMBTinX0pPppaovSZklIlBI40Tm35PkFEQpxk1xDIZsYnHZY09K1me3mAj95pvGGaczEGBiGA4fDKME8C0J4FAOQ99Cv19ja81e1Aj47O+Pi4oLnz5/T9z3ONZKwF0FJ2qaVa1/kfpKphyPBUSklkncxnKBcCu9HxunfQE/Q+8Aw+ZodZHmj0cuwUI51Nqo23BUociUDcLK5q5OTMAfD2eGZChtl0uQXBC9X1lVJCaYgqjW+NsBzXJhIAnvON0wdZ5g1Iyvbr5TCdDIsX4Bhv6Npu6UGLDVznBmRywPr+3/UGvjOY9kITqw/ljGGCgOV+nnnPttR0/SYqs59i1pEHeGlulkYJRXMuL9/UEXe3QfaLqBMXG6wFKZ5j6BkgTD9lMhlApVouqpsUQpzCSxwhFhGRa/Y3e9FsDlO3F4fuL7acXtzoBSZycshiRtHkXOfUlp0/0RCKZ7o/0Wshe3WsuodXWelElEwjqKac3c7nJwXCGPAWC+bnJ7HFeb8oizzkoo64D2DxafJmJLcJGgZkZFZTHn+aKVnqozgArlIw36qHmkK2Tg1GlUDXgqRWJR43oW0aMAmbURuTckcZ4qRaZA5ST8c0ZBS4G7vaRtPJkKKFVLNxFbTWY0zMMaJ3Sizls4q2pXMwFmtOago16sU9oNIA+7HmY2pSIi7C1lhR0tMiWEaJXFFA3UetRR8ykzecxhHjEp0raFrdR3dyeSaaccY8DGgtSKg2HnP3ocHqVyYpmq1JvcgSKWfoyEusH9dyzO8r1hkFMVxvFpW+Ul8S0NY7peZVFNyZt5UlNLLvYJiIX/lcKzO8klbpFS93pJPdE/LcseiFKRTBKnAtNsvDM/j79S+VJHgkHJciC6zvVduIslYkrPLPphnxaOUiHq+v80C65Us3p3WBKyW35PRrqq0M/nlfVhra0UXsLNAibPiYmFE8Fr6sMLI9rvheF/kwrubG7mmSrwgjTY43aBQVd6y4EPkcBhJWczKh/HANAn3Qikr/VJjEHKh9DKdE2bs7AahtaZbraQHqDWTD4SY0SpUseyMNnJuUhavQlBi5B6PvcYYg8zuVj6BMZbhMLHb7ThNMr/vUN+HnSp1BNWtcxXLPT3kFlsW05xP5LmK+Kj3ML8Wc/E049Xzi50AJCfw5Puw4QePB+9NjrncB0TE9QMizf+2j/LeV+q9nxwP9eARIJ8/nDCihC31+GqVR7/z6DW/L8AvG3bdGJjhopPrvMBX79fGyzv4wMeZlUoevPwM6ZTCNB6rXBFaePhGTxDrDx6/7eenP/uuU/C9a/kkaXn8vffP95HwdMpUbl0dpXn0blW9EU6fZ/n8HwAmHnygD6AWy5OW8p3nZL695kfMyerxcxyTuIXhx1ETdZiOiaZtmveu13cfx/u+fOAeP77nk+//DntM+a5/fcRzzGSe+Wj67ns/1wf3o5Nb4dHKeO8tfGjNqtML/uDtn6wXPrSe39tIl3+UnPHDuHy3646f6/1dhioQULVW6/uYE+e5wHnvUB+4v2FpFS2kqPl8ztf+5MOcLukH5+X0PJ+cm1wyIcSThz1WcTj5tY8Ngv/u+HfHvzv+3fHvjn93/P/j8X1B8Hdwlv+eaukD2clSyZWTBGhBP97PCh4WJd9d/Xxc6neaL9R8Rh2zs1M1gWWm7wPJ0oMkQz1+jyef8b33eHqU7/5IH3HMZf/p687Dxg8f93Dm5/8Xqtu/qmOxt4ETyOxhZSJf8F2XoF7MhzmyOp33/LdwPM5g40kfV9oMH/qt77xTPuJ4H77/YIX1Ec/z3a/+uFoTCGv5zbnv8fhe+kDp8zGXYXn7Hypu3juO9/7j7z3AXk6gz+/cesrD6k5IPf+WFs6/wUPQnOP10ub9kPAxy1L9Tvvzdz/hd1Z8D47f/hqPK/fvOz4uCCrFH//3/vv0T54TolgMaaNBR5wzOGcxpchA+jDRKKFod+tO9D+Dp+s2JKXwFKxtccaybVpKEe+y68njo4j2Bl9tiEKutjuKduWgsqEOh5EYEmFKQlTQiq5tqpZltZJBLm7bOMGKtWL0E/vbW/7r/8N/RZwmjNH85//T/y6ff/EUYy1Wid2Ic434JHq/6OEZZ4WPVdkSpgh/QlsrjXNraa1bgk+mkEpmHCd5r0EsRXIS6SmZFzI40+CnQPCBpm0wxmCN4frmhnEYORz2tI1ILXlfxXlTYt2tUcqIsn7bsT9M/Jf/5f+RaQoobfiD/+h/SLe+rJ5ijzAFTiEUgTJOA8XjYE9l9C3IdxES9vygmbo+o+OyZuZ/PKLML9mQ/D1TyWfm2AMQRin8eM+f//3/EzlFmqbhf/Vf/C/4yU9/xuXFC5x1lJTZvb1h//aG8X7P2cun5BAZdwN220PJ+Js37P/kl0xfvwEfsf2K5uyMz/6jf5/uxVO6Ty6XvpFWH7G7fgBuLHWTjPkoKTb3c3KerX/rmU+Zr7/6iv/0P/0fMU3Cpv1P/sf/OecXF5Qs8mXOWtb9CmcNVhtxoUgiZ7Xf78k545zFugZtDV5pYpR11hhHYzSr1tA24oeojYxZxJjZ7/fEGAjhqP6xKPNrjW1WhCxjG1MyZDRZicyhbC7zcinCzMwRnT05Z66vXvFf/e//N6QUMdbwH/93/kOaxi33kgggN4ToCTEwTAPWGZquZdWvsM7grF62xJxnMpdiGEf8FDjsB7rVCuscrm2YKWWiLKJonHmwH2w3a/rVivPNBmctzlr2exHmv7m+5e5+x34YeXN9zRQmfJC+pjaigbvqVxz2E//3/8t/Q8mFtuv5T/4n/0s228vvbsM8Oj4Enc7fmad3Hh95aTAd74f59zQ8TAYX+LrOPeoqYlDZ5DP/4BQ61Urz7Te/5H/3X/zPZK9cbfgf/Gf/a9bbC2H1Whm7MbrgtDhTdE7kAq1W1dtQ9itjdS0U8nIvqJP7YpkPXmzi5P0efz6zxh/eVLlAQhGzKFOFVNWXTj6JQsZANFVHuGR+/Ys/5X/7P//PHiTQ33V8VBBUQH/5hM3zF4SoahBUYI5BsFWKMgXC7R2uiPHl5nzNYRoYg6ffnJOtJRqLMeI4sTEtKLEWScPIGAo2wDQGcpLNwliNNZr1pmV2Gmj3I8FHpkEknbRSdF2DXS7OzHjKNFVOxxhN4z267Y5VkoIf/ugFP/npS7mYiB1Jt1oxjZ5pmkSaS8vAdZjnv3I5KoFQliC4cs3RckfJxjdOfhkUF/aUzLGINqnBaFeDYMS6OvZgDLe3a6ZxYr/b0zQN1lrx6qoWOn3bAwafMk3Xc3s3PLjRVttndNvnlMqim2+UZX0xBzwxnQVkCHzuuizkGITJWoPcqYrNHEDL8vPT/O0o0H36qkfcX7EEwSo+LnfR7CIBKIU2zfF9KPjk8wt+9OPnvHj2BW27RmOYbkcO13f43YHN80tyTEz7Eb1qyTkyXr3iNjgOZk2+u8O2Le12w4+++CHrLz5h9eNPmKem1Eyo+I7jQ1veHBBm3dd0SnbKpRpFs5yrnBKrbnXsvSjNy08/48nTZ5QUq6KNYdOvxFjXiENATkK42O0kCDaNWFkZZwnGLkHQKYPTis4pulaCoHGGGDI+RO7u7gghVBUPmcm0VnqSWmtMs8InxRAKU7ISBHVTFT2qiMCc84QJlSMmjaIi5OzJ51Jstj1t2xAmt4iYN9UhJqZA550EwVVDt+qwztK1rg61IGL09aR33hFCpF93tKsVpupFihNLIJck5rONZbNe0ThL1zScn21Z9yuebM8lCBrLbndgGEaaxuBaQ7O3TGVi9AYfPAURY2i7jn7dCyeiXmulNC8/+ynn50+Z2dPfd8xr+bt6iPoDQXAO6vNddHwOeUZdE4rTCkxuzSzERZUXt3et9ZFkc7KAtRad3PkptDY8/+wnbC+eYbTGWdGEdaZgNVgFrVU4jQRCa6p/pUMbVV0gzHKXHElpZWHunjLsl72kFCECIbJ9p+ciyXZLSBAL+CT6ppnjXiVbSBVjqffcOE2/rZxcjo+GQ0ud1zJaQcnVoacQiowp9O2K5CPh+o40jkQFjT/Hx5EpB3RR2H5Nu+1kZMFHdilgraZoRZgiPmTGSbymKGKOKuMDhtZorBFat4ngVcTmQKpmuEZbUXSv8zyykQgLjgQqyr6rtXkApWz6NZt+QwyJpsgNsl1tmfTEpB0k2cCyBp21BD07K19k9vs7SgQdNSW1i+qDdaIM4Sw0zqC1wxhROphlk0ARogz9ik5eWt7X6uwpOWf8GBaG6TAMYgsVArrohallG4vP9sFNlvPp0OgjQswcAMscxFhGNpYM/5T1xsm/HwS9o5D68YxSH1v1uBbs+FGlCMi0uqrxU8aIZ7agFIrqAXRdSubN7T/j4sqjy4Gzzad03Rnb5y/ZPr8EdTpXhlhVeY/pHMPVQVCGr/YQBvRuwL97i9t0uPGCpl2JMPlvQbg+vFmVqn1YQ/4pEcBUpap6XtJsOq0fOpfok+w415GNwz6TmobWWtx6jTZglaHrGkoRNf9u1eGaBt12lS2ZKSFW2ThfnTYUBrHg0noeF6kbiJql6KxUg7U9oJVIhrVWk5VoTharydUrUdjghWyArNEhkZLC2RNh8FKYhlF89mKiaTs0SmBgVbBOs2l6mQ90qrooFHTrqixdIflqS5YS1jkR0eh6tJVxrALsh3vu7u/EHcNqVl1DSoFV12GMYfQebTQ7exAdTzSHw8g0eUYfmXwQxrOUuqgKlS8zjlrziGO2mMOynMcPL5zThOC71pOeF9KjIy/9o+Oamn9HNv6HRMUlcV2Y5VXxqA62y2vUdafkHolRPXhtmUuVP6pISJLgIqNc0yjKXlFVVRst1krCAtdcXF7U86WWfeuUFKNKmacbH3zWknMV9lBw0spZjMULR7EBNe9HRwi7lIIIIikRuo8fV6HDRwdBmePrVp3QWKucjzLyJhWFpukosZBWPe+urvCHPTe3VyinUY3FrM7JUWbVYpJNQ4eMbRTKKIZqMKuK4uxsK0EmRUKYSCkwTSPZaqxReO8Zp8DhMGJNg9aG6D0zFKBNu2j7iWRRJNXh1WmaHkAYGUUsMKVEyBGTDHp0qCLSZsN+h1KKpmlIPggcaTSmleffbLZ1jiVjnJXhdX1UcjFI4F3cKFIh+EisuqSZarSZj/qhCmTQVAsMPI84tCtLyYZSXA2CGR8DxnSMIT1MSOc75XvXgjoGp0fHg28t/bRy8tNjJbi8Xplf9tHMY5lhj3xyI8ugulJzdloDXzluour0+evhx2t2u1/zTdyx271i3T/HNR1N02OtDNijJCvVGiiR8XBH83TLufsR3bklXV2RXr/h9k/+hOnqLbkEtj/5Mc3lRe0TfncnbhYvKEVQgPkGnIVHNA9F1U97cHOAPLl9l2N2dyDnCjGoCn1HsoaUq5ZqnrcQGcz2fiKXRKtkozWAaGpVGF/nE1iqnteTDXUZCK8VgSqQCcSiKVlVE2VQWbZrXVf1nNBro2oF6bBJUJjjsinc7ndMqRE1KSUjVDF6tC4oXXCtzNXpKlyQsqKUWTuy4L1f5k1dteeytqXUai0jvoJZUREZQWW0FRNZY+2ioZkzIpNWMj5GpiABcAqJEI/GzHAUyLbW0rUNoTmZw60Q/odusI9nwn7c8VA6rp5XTjto6uSxp2+wBrtl4c731/z9mnKd8kVUFSYxkgBYI5MVzoo0n8qKcRil6nOGmIOEy6zJKUqlkLOgC/N9XgNYSGFZZ8bJNcEcK9kFNUHmmCUAHJVwRM9SBvfls5YlOS9ItXjUUv3djo/sCVL98Cy5RJQRCMU4I5lTKeK9Zx2u7YilcJgmDoeJpmtp+q5uGnIhQi6UBDrLELZSEJNklwqkj6chRcjJV+Fr0UigaEKK1YcvYrSTi7lIjlHnhPRC5hG3BrHKSTE/+GAli4J5QmZ8MgWfIk4ZmbMpAlfYrEhDIKaEWbXoRmyWnBYx6Uit2LRGW3E4oGLdRhmxh0m5ZsWlQhYImF1nlEpttkjCJhJa2mhRsgDB3QGFqZuapsQsszzOPL5kdbFQF9nJD8txkz7eyB964Pzv02D0Xjknr7PEwiNEcawC67+rI8d840mwrK//MOrO+M/Dt0IhxZFpvKPEiRwLKQam8ecY43CuXRKc2d6p5ESY9ujW0DzZYs1nTEox3u8Yv31FOYy0T57QPX+OPdtitPng685vcZGxKxUQXvqpDzP+OfDMJJCi6oZQn+9RHizZetW+XNLbfFQDmckLQn4q9XdyVcqo8nuzTi1ZKqq6ySyksOXcPFRmkcqwuqgr6WWnur7m15p7wLPsVxULq5qsajEWtvaItBRgGEcyGaM0NliZKfQjWheMAWUktBplahBE1Hfm7L4GwJgSCTAmY7NaEpCs6r2NkNx0bScYY0TL09R9QNX3XEd6Yi7ELG4YqarFzOiDBHV5Dls1jK2zHO+VOQ4+3HR/W9X3XcfDUHY8lvrp8Q/Vyc8+8FvHv9V7Pymn3z9V/q/fNVpUrEztAWpV3R9TpMSIn0YJXlkzTaMkLymSUkYbS/SBYsTPc74TlBKVsJTFZb4pLbZtsMYtp7SUsqALC3Ra0aYZpVLL6pOKdRaqX26X+fvA7zLY8NFwqLFWZrRSZNYpFx1LcRcPMVOURV2csfrsJWnluP71ryiTRwObrsX0LbmzoAROtY0FI4vTNS2mulXPQ6vOgl13UKQXESmMIXIIUUxAnasZacE5tZjh5qJIqRCUvE9xiRdMP1n7YGnYYHDBEFGYTm5SX8QSxmXD+foMnQoqJN59+YrBR3781/8arekw1nB3uBEIKhdCjkQtGfQ8PWXnyl5XqE6parlzlBEydRNI6qg5OfcOQ4kLfLXIOVEIWYap0YWi5wVxeqil2nqcsR5v48eBLZ1swie/UZZ8jWOYOz7byXZ68oeFENKvDI0Tt5HDPhKCVP3z+3x4w5Zj5K6QyoNX84o4FWLypPAtPgxcXP0lWv+Mrl3XZ6rQYsnEOLK7+hLbbHHNhsuf/5xxe4Y2lv3/+f9G/uod9mDon77E9RvM84sPVtBzwMuLtnoVfJjfqz5WVzNkXE7haR4GzbIEs/pRS0UxEF3LGR5VNUDFGI7Q0vK0s9tIZprqmlOlksmqJ2Ydrp51kmdUg+XzSHBLpYgmqirEIslJmQP4nJRUKTnI1edR1e/Ja2rzMBkrpXB7f08bWhrXEbPoQPpwwNqCtRCU9AFddrXqKVXUfg7+LAnI6Gd390NVQKkBy8w6xkZ8TDsn1mttI/30ameWC1VJKonrSYKQFalYMhZ0g1EZQ8a1Ar2u+5513xPGY+9sDoCnLNd/nQrwe39Tvb8c57vxewPh4ld5ep/VyvJ4Y7+3zp0GJ+pyOC0m4cN+x83VG8b9Dl0CcRrw44HxcKAU0TPebs9Zr7eUmIgh4KvDw5xIyKKV3WhzfsZqu+GsbVFQ1YHmZIwlkTX6aHduDUuRpOdEtH6sQkF0wWtbQiec+u2EmPn4uCBYEHNEf+xZqVIlonKSmyUmgR1jxK7XrCnE+zs6peisWRqXSgl5JRXpfczi0Bq9eLhNVUnEObXsqYuE5Jx1VjhWDFxrQFWagqoahQrIaA0GkT7K+VHbtYDzhSaCco2I6hoYvHiAGWXYdmv87sCbV6+5/uYNPib853th0dFhaVFKE5XCp0BJER88jZXkQNNIVj7DTYraUBbbmBxnVQU5qUVJdi2QW0EjAbZQHvRzrJkrquqU8J0jEXM2P4exk1W/QCH55LEfYpMuYWWp3Y4/1LUKLMtP5Z+JrnOsVo6f/95nKBLDsOPXX90svoPvB8DTTPsD0EZdh9OUKB7ceoXRW6xdSa+XyjJDKoi7wx37w90iswSgrMF0LW6zwWQFuwn/5W8Yv/wGe35G8/RM3LHLfA2O52xpucyrSMs7PS2ehSF37IMsr5uPwW+BH3n4e7MsqtHid+iMGNJqpSqUbCrMa5cANkNPVs+EhKqTWTIhJVQSyEvMSY8SgvKaakmsUJqAJhVNrLuLCE6L9wulVgVF9GcSCoq8r1IrzUJ+0MNVyEiLc00dwgZIaKMwVmGcWshggtrMm3JNZZR+APEtlddsGzTLf2kwRuGsxjmNc1ZYtfb43ErppdKIuZBKZSJqizIWbRusE4s2VKHtxITZNY0UAOYh0nK8bup7//1dx4P+3neVgvUBj388J3rvV4QnieSj752+LbVcrwIfqpiEJCBryE9cvX7NsL8nThNOi/yfKgpnhZmrFAQfuE93/PIv/pK7uzvu7u9E6cYI216QREPbdZxdXrA+2/Dk2TO6rmO16tHWkUthGL0YhZdC24iLUOssaFurw9NzcNwtZiJVUdK71HyXJcL7x8eb6k6R4JPIEWnZ6FJMFCUbdYgibxNTZNX3uMbCfkebM+2SvRYptZGML+YoGnglo0sLKYn9Ropko9A0y8dY2GhFiXSQTiKVo6UvYZ1ZGvbRB4oGpZJo7WlVg2xZIKl6tdFTwnkJosookirswgBWRkG2q56buwNXr95w/e0bYsqM7+4o2mFxuFXH7CmY4kTInpgjuXS0Fpx26FzIKldlFBYWlVIKH+bms14g7XlDkG0lLRJys6aiSD5VTUQKpZjqe/b+sWR+H7o/UDz84VGv5PSrORuV33+YWZ6cSsnqVA2EpdD3DS+ebfnbf/On+Gng6998xavXN+yHhCy907rtuGTLyes+fpnJR6YpkrPifLPFukuaZoPW0otSWok8W5i4299yGO4pum6o2khF3jhcv8ZgSGMg3L9h+voN7sUz8t/4aW28HyvZnMuiaTlXf6fqHXPFJOf7/WtwKoc3+6s9DoJ67mMqsFak0Bpnl/GA+blnUtUMFSolgsTWOglIJaG0IAoxzUbN4uIxr6g5KTjCp5CVhqwrHR1QBs3sRVkTLQpaV+gUhSrVx7IUOQcVvj1ZfGLF1LR03YqYxFJIozFO4Rqx0Zk/U1rmukSEfnaLP65HyRIkqJklATC1d2WtXpi11gl1f2ZHSjUs5s8x57qfaFDSPzQpY10j11cjQdDZ6tZiH/kJ/usdp+tHnSz199YNH15P8zl6HB2PQMTDRP/4gLL8vdxtj2/j2oeb1X/8OPL29Wtx6ShlIXpZ43DW1pfI+CkyjQe+/foVr19/y5s3b4gxVFeghrZtaZuGs4tzzi/P2ZxteX5zy/nFBU+fPaNfb0kF7u53jH4ipsRq1bJedZR+hWur/qo6FkbHoHg0CZ8TT/URoxHz8dFBMImkocwbiY+LUJ6tQC9T8sTKzuyblsZauufP0cGjU5TscoooJiJG4BltmTv2xUdKCBTvwdaSzFeYBmmYpiRK/HPvxClQJaMzC3wo8EmqFaeueqLy3mNIBJ+XDasA37z+hu1W81Q9Z6V6WmM4hMSm6TlbbZj2A++u3vHlL77km19+Vcc3WtzLT2ieveBnf/B79JuObr3CpomiEsVEtCmgMzEEso7opLFOHOZijDSIwPMwDctGNpMyUklHgV/Ngxt5Zk7lWAWpc6SgObEJ/e3HSXx7P1VS3/WDjzikctJGcX6x5j/4Oz/n7/7Rjzg7s3z56zvevPqGw3AgxgzKfeezqAfPePJ1Kby9vqHtO56dn/Ojz/8WL1/+hKdPP8E5kYkbh5G73S1vrl6hnKJbbXn++3+XKMbqIsKcI8Uo7JNzYRv3G+wPX6CfX1T6/2m1dnQ3WGad1Afe7+m+M9+nC8RTnerna2r0o8pC0fcrNuu1zNc2MsvWd+2i9F9KWYgaog9Z6tyo9Eratq99kAxaDG+nKTBNiVhkPlB0LY+6i9a5OkKkiUVcQmISpAIqOKvka6WMjGqoQs4enQq+zNWrxhmFLuVkxk+O1aqj7RqMFe9OpRONVTTOLojI4x6lXOu5wj6KoOvKxpmTAXFi0VgnLhmNMzjrRLRb28UXsNQKMMVETIUQMyEVQioiyqw02lqarq2sUEW3ctJTbOxCBPzXOdQH/n78PXhYSc6p8PvP9SjIfeRbKyhQx9ElWZuneqmFcX+HtQ1FGfI0MO53hN2ey8snrFYrDrt71v2K87MNxiogE5PHakeYPP/in/4L+rbhfNXhms2cnXB7e8fhsOPt1RuclmRle3bB9vyCi6cv+OJHP8a1LT4nphAIOaG0YtW19KsVz549o+tWrPo1ZvZi1SdncUYvKq5Yltrwtx8fGQSlcR9jFAurklCq0DaNZAX1BJILCk1Mou7eGoeqGYSpjxHbEvHUo0KCFFWNGhNK5WqbUYcjQ6yQa70psiQ0RkFTm6e6ZFwtoXXOpOBRWVNqT7CgSVmTMlWJ/HhMKnJIE/b2jmkQgfCQEqVZY5RhDAOHYWC32zEOE2FKjGOk+IIOhd3dIIPHUaGsxekWKOiiK7pTKpSbqzByEYHYeonGMEpf1Vpmd7xZNFs23IemvPPXRVWm5clGexoyvmsBLH2M3xLnPnYI+PFdqBV0jeGnP/yEz15ccL5tubt5y831Nbd3e2IolKI55nDHt/GQ3fbhT5FKRKlE11rW/Ya+P8O5lpwTgz9w9fYd4zgQY+Rsc07btKxdR/Cp2sSI47ZpG5rPXsKTJzTPn9J89gJzvq4sScWRbv7o/L33Xj90yEnOCxGmPtUyHmPrPNWxwusaR9c4jFY0zlU4T4aQtTY1IRKyhzN2uea5kkLmZEkcCDRKZ1nvWQPV/DeXanF2rGxnWxsFMhOGwmlbq/m5qpDz4IzCKcSvj7nqU/U9WzSKtrEnZ0H6j87J95SuAhN1eP9x8FOn531eIYsXXfXuWFiHdQ1lRakORXPyP4vLz4tdROoVqQ5cpzyPJUlisoy1VMKfqqMi82D3DIv/qx6nOMvDwFeRhdPHni6s8vDeev851fFxCxrx8I0+HD6ndkBkDcz/O329NA1M+3ummInjgTR5EaXImehDnUWNTJNHh0IhkUskabEJK0VGJdpWHIEwWoTk9YyilMXqbpw8ej+g7D2rq3e0qxXaWXySxC2TCVEYvEVpurajX4807sj+n6+1kJ+Q61Wv78ceHxUECxCix4epTi9GGUpftaIkoAqkBBmMcYw+EErGoLBFeoErBRmh9A8xkJRBr1bUM0NIE5mCddC0ApWFGDkMA8FHFGLL4pzDAU4peqXQJFQuWD8u2XYcBopWxODIuqEoK8O+BVJ5CGv4XnOnPDdff40ZwRTD+skFm26NegK3w4Hb/Y77u3umIZCjAt3RNGvatufq1Tu0Ldi28OOfPGO1agnKUEqEJDRwkIwzLhtRIg4S3ENJdF1H57o6NFsh05oBz1XIvFhPyRESYKUvG/PHX/TffrX/1e74UsDawvm25T/+D/8GT88cTiX+9E//Jb/86g2vXt8zpQ0Fx6N8th6/JaVVoGzEdYXtWUO/XrFarTDWcbg9cHd7w5/9yZ/g2obLZ095evGCftWjUAQvqjz7wwHVJdQmYv/WX8e0Dd3Pfog936K7VjLQJTjUwPXIRkc92M4+fB4eH0bPZsEanZLYxpw8xbpr2fRCFHBOV/KUiCpYa0G7I23fmMocTUQ1OyikoxeeBl0EytQqEUxk8iMpQZodvlV5VFFk6UFaRdM1Yp1V51dlKxXVEKsLGkvQEVvHfKw19F2LIrPru+N1VIhFTuvwfkLXqXBja6/zdFbyBGZfzm7tAQrsaZafSakt+0bOQC7kmKUidKVWgPKcOQvTNVMIRZGSGK6HJGMRPiZSHf6frXi0qRWkEnKQvMT331+/rRc4z9s9CPrf8fun4ysfj+981z17PK9L6KuJweyZffrYNNyx9xPvbu8Jk8cZy7PLS6ZpYr/biWqRnxgPO2ISH1BtCmRpj4XgRdxkvaJpnOzDKNrWoVRBqZYYhWBm256sHQcfefXmLd1qxfnlObme8ykGxnHiHsXt3b2Mq3QdXdvjXMtq1Us8sBbXNrJXVmTh1C3otx0frRizWjX065ZMwalG1ANIhMPIYZxQykkD2Xbs7g+U4HG6sDWatm0J40BgYlKWoDtwmsa1YsdRhLxincG0jqycsDtTJoRCDIlWK1YmslFgwgTRk8cDOQyoktBO/K9WzvGDl59w9uQply8/45ffXvHu/sCv376jKE1J44PP1q871udrAi1vfvWW6TDxzBgu9wNhDNxc3XDz9obDzZ5GGXTjMCUwXr8m+D2fPn/O7uae31y/wvFzLp9f8PIHl0QmEoEphcUWJoa4wL85Vs+5kpmU4Opd28rGm6Eog0KTat+nID6KusrCIXO9Mo5Bg2uaB+XJMVN8fHM8+t6MsZ/8/JiRf+zqmAEW+OEPXvLjL57x+z96xuvX3/Cnv/qaX371G15fHwj59KU+1Ft8/L3HbX/ND17+HX702c/4/JOfsV6/AAyvvvmGV7/5DddXb3n67Dln5+c8ffGCru2WZ7BWJJ58lIQKa7CbjVSF2w1Fa1JMtSLSFKPe1/Ms5fs1dE/f/UkucSxwpfdrdHkvsM5MuJwjMSoZuSqKmCNaW7RpFtp/1kJgSnGSofic8XlWfzFL/ypXJiQl01iFKrb2juubywk/VcalVqLAYiy2BFkXBiLzRauYSpHt1KmMcRWmVQmyrwomjwg/UL8XBfjRGm1yLfDm0Yu52pT3sayRGflBHNUVbkGTRPSiBtIcoERWrqGzDa5CmrNDm4+zMS61J1iYxkmCoI+LOa1SSRjmqvbttcZpQ2MsrrLL/00cvzuztDz6+2MeX/+UyOMBqeVROTPevgXtmO52tN0aqxTDuOfm+ppxHNls1pRsIRlQkbZ1nF+ccXt7hx9DtdjyeD9wdfVKrq5x3N3dy3hE02DaHtt2+CRo1qrrsG2LtqKKlLWiaIHrl+CtpFd9GD2HUQyUrb2rkph6mT001qC14dtXrz/69Hz0sLx1GucMmSLaoBRUCsTxwHi/Q/fnD5rHjymv4zgStSVaRbZiFDnTxXWRIV+jRXHCV41ClQsWgdhaEk1OWO/R057iJ/KwR6UJQ6bB4lTEmcyL7YpnTy/55PPPGIMwEb+5uiKQgIeiqq6qUNBZstNErTDOUTLEwXP39pr99R1+mGRmUEHwI+kuwrjjZd+Qhz3h7p7x/sC0XqGzZPIF2cTmQfiUQn0OwdILiIJCSgQfcMYIBFMENhD23UwEkKFgk3Q9N7Jp6EqwUR+sVlgW+/zdBWJ5sEEfA+P81eNAePocD/XYpUpQSqqdT18+4YdfPGezcnztR67evePmfs9+CKRsHsAz74fBhyHv8daglOLF05/w8tnPuDz/AVo3TJPn+u1bDrsdKUbOzs84O79g3W8WOLokgTiVVthZV1YV9GolkFy9+UrJ6Kxq873UzLXOK51CWb/LpvWhG/EE3j751kIem0cxFg5AnuXKNDkbIX1RKCmQK8kmlnm+6tg/LqUO12dpTzijqolsWfpBJcvvaW0wFa7SJQIzZJ1PNstyDIiKylqt7zsnclF1vOm4ltSSHuXa3ywLzHjMFh5Ol0oUqiS2XKuoUpUhS6lBVVirwg41OKNYtyvpSeZ5VFzeX0rSE42+IkVJWI8hpTpjKJWx0aU+p/Q5jdbYamZrv4Md+ld1/CsFwvd+5fE3ToPlXFXOTGl18uf4eD/cY+wKQ2K9arGuqdJliZhCFR6RWVJjRB7z8vyMkjNaKfa7e+Ik62a/30mLyjT4caiEvkznpPcachJuRA1cM8N9hjf10puu0DxVkznKvepjlrWuFcab5Xe01uwOw0efyY8elneNoekMKYGjYHMi7Xcc3r3m5vVbnnzxIwxrkjFsuhWq61iFA2m45/Z+x9v7PbY/Y/v8nKwtOUG83dEaTaPhDLCq0BA5pExIGVcCTzowraGJHr+7Ybq/Ie7u0SXTGFh3jtWq5cWLcyEeKM0XTy948ckLvvjBF0TtWG+veHV3x904EPzhwZLpTEPfdOgNXH7+krNY+L0f/JTGR+5/84Yv/8E/5c1vviVe7wBD1Ilvvv4lU/YUCtvpjq7v+fxszSpGzH4gXt2gV2BcwY8iG+dLhXS1ptMO21h0UaRQe1XTxJBVZRrA5GeFhUJTKca5QnUmGFQW2KZddZjaI3p4yeR/0iY+CtOWJRDON8fp2VAPfl8e+j09hvrzVApd63jyZMPf+w/+Gn/w05ccDu94d/2O12+uuLoeGCYDZSVLTun3Xu+9BVff3imDW2vD3/nj/zY///2fY4zhzatX3Fy94y//2T/nix/9iB//0d/kxeefoY2hZFBGUVJmGA4V6tKsVh16UuSYmBX0lSDXtXenKptYLf0wgeN+941qSULKDF0fdUQfn9d5CN4u2otHKKyURJy8YFclo8l1lEIta2SaFU/mYaoK6YqElczzinyfqYEhEWJexgicawgpE3MmeV/f/zHQLBek/ncma2ktykXzUPtweIi0kCOqGLSS4Xi0Fii2BsFcahKFqoxNWf9SKCoBzrOCOQgqRbGmPla4Bk/P1jy5aPnk5TN2w8i3V1dsVhatLClmJl8F7KuhcQie0U/SPikFZeSaYwwGSQhaZ2lcQ9e2rJqWfXN4+LnqOf6dmoUfWD6PHVEe/+y7EZnTtsX33UsSOpQqKJ2xSsiDPsCiHlOPnDM3V2+4fPqST54/5yc//wO0tby+ukLpxG7nSFWSL6VIYx1n2zV/+Ae/R4yF/eHAP/pH/4h3b7SY437lCSGidSAFMaUO04htWpq2JSdPSQ2agCKhtaMRxXcZQVutKEqTMZWsNqsbway+NZ+/RUs0ZtELnvzHXBHgd2CHSoY6yzbJuW2dQ5dCmA4c7m5oS6Ffb1BOBtJtgsM0Mt7tcOtzmrNL7PaC+71AESammlEWVtM9q9awWTXoKI3Xcr/HpIBKgWm4Z9zvGfd7utqDuDjbcH6+ZdV3nD855+bunv1h5KtX79iVjnt6fnl1y9v7HV5bsm1QrjlF24RJZi2MXjY6o+m6jtvXv+HqF1/y+pdfMewOrFc9u5DwuTD4AFp6GzFnnHM8u7zk2faMrmlp9wMkRXbQI7OAQxT8HGOh03VAXtGqhqRzzaxWJAW+JFL0xCgEpKTifAUWMozVbtnYCywkjOPVOt4Y8/WSzffBg+rPjn2f42NOqsDHN2KFxOa5QGMVz55u+W/9nd/nB59d0neaf/4XX/GbV6/59uqWKVhStrLcTm94dXrzPr6h389USyl8/fZLmo3Bacf1qyv8YeTFZ5/y9OULzp88QSlddW7lufw08fUv/gLbtLi24+UXP1hEHkYvrvVGp0VoYSFV8D5c9K91zCzoMp/jR9drDmC6VPbm8fcKRVitYSL4kewnQUe6lmmSweT9cDRUVjWQL6IMWovSv57VVGxdM7nCf0LWkYxfqiGjhXhgrRMo8pSJp2piUKFL0SxN1SH9Ue9M1VlGfdzwJQc6jnzXUyPPr2RtaYT81toGXSyqGPmMSmPbriJJmTDuGe5G7qJif3PDbvC8vtnTNB3bsw3aKumfpkgMvkouToxexjWKQuQOnUUrJyIDJ6MVM7r1fiw67Vt+f4J0XMnvP2YRl/6u3z3pD578FgtDEJhF59+TDVPzYzLGirOPSkGeKylm5u/pa3WrNWfnFzx/8YLLJ5e4pqVb9zx79hQ/TfhxInlPniZiHDEKvvrVl8RcCDFxdrYlh4EcBpw1qFKwrsHMo0vek8LAeIBYNLm1lDCQXUO2hpKLSE86R9u2pHl2NclIHCkd2dJ1TleQiApqlwrX/Q5Wch8dBHMNgCkVEgVDoTFW+hgxMO7v0c7RkKl9ZSHCTJ79YeT8+Re4/gyz2hAPgZAiJYmNUi4RHXZ0uuFsBTkOqGlkuL9GJw/RE/e3+MOEHz3r8wu6xnF5cc7T50/p+hXNuuN28AzxwDdv3nEXLfeq56v7A3dTwBdN1haMe3DhdR2mLdVySGAQy93tHb/6xa+4fn2FVpqLp0847AZSCAzR47RohIaU0cawXfc8W69pG0OaBkkYIvQWfCxoH8kloWzBmAarLM5oGi3Mq6wL2vUERdXhGyWLVgLnZaIIINcM2prTG2SuJB4ec44tXx+/++CrOQCW8uDf8mX5/ky0/qhpLc+ebvlbf/RjXjxZY1Tgm1ff8urtO65u9oTUkHGg6nKT3eMRyPj4+I4g+PpXmLbQqhWHt/dYDD/9+e9z8fQJ/XYj4gMnR/AT3371K9qup9tsef755xIUrCGPqfYgNNZpjCoLLKlmZuHydr6fDPNdn0Cg5RN24XKeH53NOUDOxBXKAi+WnPHjAT8dmPb3hGkQIft8xt3uwH4YubvbSfWnZ9KJqoPiEnhsJc1YI84Is0BDnofG6zwgSmFMbRGwkoBQK+E5aVnEAJRa4NhFkODxeqkb8WkQ1PqISnwXKDHDmRqZnVSlkMIAxuFUL3OECaZx5JADZYocDpHdlLneJ370o0C/kiBfsqhQiX2Ux/tJtIhLqRrIClPmoPf+2MZMJvnQFZ7j10cPyf8OgfD9sZGT97CMOsxrVL/3zHPdPqsIOacF+SgFkpYK+MFvKdpuxXqz5eLyCZvtlqZrWZ9tlmvshxF/ODDt99zfXxPjxNu3b/Axo7Tm7OyMHLakMNCtWoIxtO2KECwhRkrJlBwJ056sDCm0JD+Qm46UBO2yiupOYR/Mqs4jS/P4zCnHsVSSRFGV9PNXHgRLYX8f0X0CZQgkDAlHQTc9Z5fPGWOkjCP65h2r3gKFN1fXxJBR7QVudYlyK8aQaNuWxijcuKe5v6MZ96ztxBbFZbMmpBG/u2H39Zc0zmGtZbXaok2Ha3ytGC0vnqz57LMXmLbhF9+84utvXvGrr1+BvebZFxkuv+DtIXI7et7dH7AK0rDoXlGKKNAfhpaYA85ZUjD8yZ/9in/557/kz371JZvPznn58hl/+Md/Df7sl7x+/Y6rP/sKgyNnxzc3d0QtPdMff/KU836FM47RD8Tg6WPhXDk+MWekGFAB7D00K4VttKglqIwuivbZz0htz+h6Xv/6X3DYXRPGG0YiPmf2VVtU53n4P1eiR2QYxocb0Kz2UR56iP1VHErNg9IZrTJ//Nd+wt/4/R/wN37vU3Z3b3n95hX/4k9+wW++3bMfHIk1tbt7gm/OG+ryrCeZtYIiiiXlZDHnnPh//6P/K1/++iUX+hlfvPwhL158xssffEHTNMSYpJNVI1lMmfvdjj//03/Bul9zdn7JT/7wD2maFtdYnHWEEgiTl4CBOcn852bXfA/w4bLwO74/08EVx7746dD8+9V1hXoQIkvOkcOw5/bult1ux+76LXEaSNOeTefYbrecnW149eobfvPta169vsZa8b9bb1Z1YFwTQ/WgjIm2aWibhucvPiHlwm6Y2B0O+BCJKbPqViKU3/as1xvOz8/Zbs9p2471Zis6u0URT2Co2eFBHAUebdoUCpG5/zfreIqqD0dEb6lgZt3RTCqKEBK/+vLX5CBVpnGG1WrDE92Si8F7z9dfv+Zya7jYONqm4/x8xbOXZ/zw8x+wWjdc3byGnKtTQ649rYmYJgqCqFiraVonTNZGZifnBKKUIszi8IhtuGDVH1gTj9fCdyydf61Dgcx85qWilmO+X2qQVAXXGNrW0DrFzc09MShU2Zy8u/qVAmUdRVti0vhJrl0m4lxL27ScrbdYCoZMjJ4QJvaHHa9fv2GYJvq+5/LijJ/89Ef4GNnvB7wvDOOAD57hMDBOB4KfmEIkxYmb67ekolhVz1ClMqokkjOYpqNpO1QrzikxJkLVmU/5eB9FdWwNRC1J38ceH18JJsFgUWVhU3mVQWu6tpXmdFFMdzum24lCwiiLy9USZDxQyKgc6EqGGLB+T5MONGWkt4q+0axah1NgVJb5P2dpuo6ziwu890zTiM1BNAIbR0yR6ZC4envN7c09u/s9ujFysgc58XNDt6i5cX88Qgj46Em54GPBT5Hbd3e8O+yYnObzH7zg7JOntC/OWd2e0YWJomfmXUE7S7/uef7sKat1R9M5rC4iNK4ypiSMEpaZ0lZmKXPBFIVO4FJBZyEKMUWhF/drztotznv24z0pRSIJVaXSSmGRq5NMuZwWZnVBz6MVGlWyaIuWh/DKUqnMMNS88VclkA9losffF1PNrjP87IcveXbR8/b1t7y7es31zTWbzYbt1rD3E2EnnnQnGB/L1nAaE5e/58rkUSVIIaYd0+jYh8h4ucbHnimO0h/SebG/sdYi4gkJPw6kacQPA1//6ktc01bSiVqgwPldzZ/xaDW0gHZzWffwxviOAHiMceXRnw/vmznn2i+UaiqmxOFwYL8/sN/v2R0OlDBC9Oi+qSamilgToZASKI0p4JoG11is1aQcyVlQCFTBWI2PgckHbu/v8UnUU1CaXMWzMxllwDWOycvoUtO1aOVAmSWgq7rxPKxQHn6uUu22lHoE0JcjinE8bwKvZ5SYThdD2/Z4JrL3iKtE4Ob6mqIsIUT2hwP9qqeYFS8/fclme8b27IIf/vATlFHc7W+IaTiBa4+QrdYi29a0omjimgZnLcZYuX8KlTB1lJo7/ZinMfBhoqk+8NVxNTx+nu87HrcmlJJ7fzzck7OIp/er84VE9fi5tUIEEVQm1X2QYqsSz3Fed36VkBLeB6ZxYjgcSNlhHKiiwYLTVmzlVBHJO6uxzhETTH4SE+KVo3GGP/yjP2K/H9gPgcPhgPeeYRjwfiSEickHlLJo42i7LbZpWa1XYtKuIXuPc05s9Jwj50xQBatVnfs8oidRQZmNd7PC/Q5Epo+eEyQLLJcooBJZJTxSAq+alo1tSD6yv7rh3dW3QOInP/0pJWt0hnT7FjM4zKpjZQ06J9T+hjYNtCqy7dZsese6b2lm40an6dcr+s2WZy9fSPYwHhhu39G2lq5r2e/37IeJ33z1DVdv37G73dOuG8bDwP7+Hl+ZmVrNdtjxwWfzweNDIBfF6BP3O8+f//obduOectbx6R/9jCfPzjHPt/T3F/RezBpTSsSSWK16nj9/zs9//lPOLre0ja2y1QntIaWA01CMwimZ78o+yOJOBeMTKgsN3d/ucfT05w0X3ZY2eMrtW6Y4MaaEWpllG40xibt9U2/YR95iau7b1A1ZSSNPfrs8vKnIqso3VWisQn9Cfz8Jmie9sqw0fd/y4umav/kHP6JRgT/55/+M65trfAx8/vmnJLMn6QO3w15cQ2bO+unKOi22lq/lvR8pk8dc15iRUu4Y7nYcxo6d19zdv6V1Zzizkqy+qqCUkikpkf3I3d0dORf6f/KP0VbUap6//IT1Zsvlk6fU1Fp6CjkfZ834ANRV9/CPze5nljTl/V7gfMyC0UqJTFuMifv7Hfv9gcMwMEweXURpqO87+lUn4wSqGpEah7IO4xo2Zxd0XYNrNPk6k4eCVoWu71iv18QcOYwD17e3tP0G6xqarlsG9F1raVct/bbn+voWnzzdqqNpTHXZgFMLpuNn+lDNI8zQhfk6Q4tlJh7NuYUSKEtJ60WbBmMNl081w37PYb8nxEnYwNffYExDypndfs/5RQeu5ff+4Ge8fP6Ely+e8OyTTxjGia+/+Y0w00OoCUEiZaladTXN7VY9q1VP13WSRClTK0dEwjGLRN3DK/cwsZk//4eAgQ+Hx4ffOSZdH36UsJWFYRti4N27t5SSMFrTr87eL0qVjLQYrVitGmIY8NNISUkQD2urXP5DtMN7zziO7Hd72s7R+ZZu3ZBsweqEKjLiY3WmbWVue709o+lWIqaiFX3f0q0azp8+4TBM3N6L2Mj83Kla23kfyQlSEnu9mfCSqjF18KPEC6dpO5EFnHQmZU0pilTUsgZTqAlZzkxJ07m/4iAopymRs2fyAdcatFUkLVlJ8iPlMJIjxKSJylFy4ebdFYfdnmE/MJFFPHV7xrPtlsZqdB7RrmBazcXlGZt1L+7wVmSLnjw9Y32xkZ5fJ/BbjJmLJ+dsN1tM2/PLX/ya12/f8fVXrzmMXnRFCwy7e7756ksO257cOPpti0oZn+yD677qe7pVzzRGxunAYRxwrebF5imr7gWffP6Cft3iWsfLz15g0PzLyzXTnSf5yGbd8+TijE9fPGG1shUSyhg6cBYjuz9YSRZyjEz3O8LoyTER0giVCnD36ltaHzHrLYf7PcPdwHC9Bx1wBgiinCObhF4WjP6Ag4TWRliSIJvLXPmVsgS4GZOaRwKqzbt8L5cKXcpsmGxQiVIkQHau8Hs/+YR/72/8hA4vGH/OFBJdZ/nbf+sP8eUXfP1mJ3qPRVENKOFBp3KRATgJeMe+Uzkdu6GQ73ek4klTZH9I3Nze8Mu/PEerZyguKMWyWvVcXl5yfn7GNBwEuhl2DIc9v/wn/w2qacG2FD/x5NkzNmtxMlDGoFMCY040ZtWjvz/0r4+4h9Ryuj94CMoxoWZ7mJxZr3tWfU/mBX46oJLHpYHnl1uatsU0DReXTwjZYNsdXduy3qx5+clTmsaidabowmpc4RRs1xv61Yo3VzcoY7Btw+bsnK7v6bdbcVBQ0DQN24stZ5dn3B/2TJPnzbs3PLlQ9P2mrq8q5qC1sF4fVUrzMUOlktBU7bpZlJu6ElQRdSdd6fAIiWOzWvHHP/s5fhg47Hb86le/5u3VDbfX3zDlHdooPv/0jBfPt5yfd9y8+zXEt2S/Zj9cE2lwbk0pN4ToiWkilYSqs8vONfSbLat+Tdt2GCdmvqpQW5lFZnbn4fyTQxPRRBm4UlruIcURKXjU65xRX7XEzaqAg0LGth6uN6VYYGJFoBDIeeDN66+4393z7eu3vHz5BduLp5jaDyxkkmS1QGTdQqsL8fqKu/2e+8PAetWjTYNwjAvlEZFJK0VJkWnc8Q//wZ/gWscf/vU/YLu9oGt7YvDVZqnQ9yucc0vSBkV6yUr8MbuupWla1pszpvFC2MPDgE+JkBN59qzM+nhzzAQyxEFeW4dpJOHx00QKkemww08eP3lhcisZ37NaoazCGkP/O1A+P7oSLDlTkmTWpVJrYxGoMyktdkIFinZgRIX/MO4Zhz3TcGAfA9lPtEBxVioYIspKFeO6Fm3NwnJUFarQld0kWRBYo+umrPAxcrfbc3u3Yxwnma3SMmA+DgPq5ho6i25FUBet0acZgqoqNLYh1sa90Yqzs55+1bBZt/RVSFcrRde19OsV3bolHhLJR4wWuyRT1cull1Ub7tZAqXJMVapLZlkcEOrDy5LN+/GAGvaEcSdU7pjwQ6A0GaOUfG5mWx+RglI1+3lMSJglunIRyHrx5DqlXc83mkI2ouV+KOJnWGZPiLL060pRGKV5crHi+eWWF0+2+HHPOOyIcSLngCqGGBNTSIy1rzDDsycv/KCXUerzH3sa6qQSPL6tPASyQyjYccc0am6u/hK4Ay4prGnbNeMwMI4jh9trQqiedEFsYIxSGNNQgieHSaTHpFyu9+LJzjWbDp8gueWx2sr8ow9UeTN0GIKXjDVncUU/iYYFqvLP7OAh0J21ZiFutY2WmdhoWG82WGsJhQr9ioVQU/tZc/+r5CQMvaahtYZV29A6Vzea+lolQ84YBGYyFeoqORL8JLY4XpjK6/6cplmhOSE4PSIQPT4DOZc6BsHy9/KoObacIONlgUoFhdhsOrzOqDTSrxyrztI2lsmPaAXrvmHdt/Srlmkaubvz5HjP/aRRdk0u6yoGLfqtIIoyzgkdv23aqjdaxcpPMhV18j4/WJzV/5b5g6jjqqi31PE5HpybkydT809qAFD6wcspCjkFYtwzTje8u/paqqoxopWicR1qcXKpz6XqbLVVNCpzuN0x7PYcDiP9qlv67LJeHxHqSiGlyDQdePXqG6wzfPrpcxSakgq5zVijqrm5YRZinxMjQZPkmhtlq8ejxmmJEc5ZxhjxKYuebxHh9uXz1h6nAGVyPorWxChCI13TEIwhqUIcD4QYSClhrYyMWatBtfjh/v0L9h3HR8fLGCIxxJM3CIcoZrp6tWYaJlQGZxuMN+Qxc7e7kcBpC3GK+BCYxlGG4p2VQV1rUU2L6XqyNow+iGh2vVEP+x1TmNhspUo0XcP9/Z6025PKFe+u77jfDUsA1Epzt9tTYkRHz8sXFzR2g27ELd749qTPA41paF1Ltpp112OK5cdfXLLqLF1raGwU0+BcwBr6ruH86RnxkDhMiRwC02HP7uaKbt0J3do65g5IwaCV0NKVNsJKtk7mX4pITiVlyUUTp1v03uLvr4ipEJNidzfitlagG62JCkJ1AhBbnIJW7TJ/Nh+6Lj5V6sBprfxmlfgZmhNafh3APyE2KCSwqirJNS8XVaB1Df/e3/w5P/70nE0DX//i1wzjHWPaMcUDaYL/5//rH/Mnv7jlm1cHCusPCBA/+veDAFn/OwfOk4fGd4FiFe4plDgy3SZe7f5rijkj6y22/wkxtEz7hnZzQRpHrm7uGEOkGMPTJ2f0509YnT9j0za0WkOKwiMyc3Zeq4BCDYiz6HrNzH8L8yzPNvNIr88Hz+31G1IIQGF9/uSRm4nAyxnQRbRvBeoShwStC05VohCG9fYMozXvbu4I4wE/7Eh+IJDwOnP9Zi/BTGfaxtA5zaoxOF0wyZOmA2kaKd5z/+4tY9Og48CmX2EbRwBuxpHD7R13+xHvA8Po6bst1jZ07UbQhEidfZRZUYqY1Z4es1an0rXCWq4tnCY8s/fiEiazkCbG4Y793TU3715j7cjZmeXzHzzj3bt3pBTp1w3n52sun5zhhx03tzfcXL/B9q/pN0/48U//GB8qqz1KQtY2Dat1T9N29H1P17ZY58TfuuRFvD4jNm2SFD76XMqSsDVoV/n6TFUqnuccOQZVVUhqdn9RPBwXikvipcqsCTzLrGX8dMPN9Su+/fYX/OIX/5yUCi8+/Tmrtmfdb+H4irWFAW1j6BuwKXLz9ltu9yM7H7l4eoazImFGzo98SAukiB92hHHPb776BSjF5eWWF7sDZ2eXPHv6nLZrUV3DNE2EIKShrpOqMKVUTYkVOcXappG5S9UINDuEyBASt/cDObEkD0pR2aCyNwkmpckojG1wxtLZht4adveWm1e/4duvv+bV61dM/iDi852l7864vXnHd8Iuj46PD4I+kWPBNk4YYrHgfcJGhSmWYlqsgb6xdNuO1CRG7UWqKEJWAhVmnRjyiEVztl3RtIa2dYyTJxqRLLq+vef2+oZ3b9/R9Sv6dY8tQmvHKb7++jUFhY8KZSxd37MOicEHvBeii67EAOMc2jUk7TBoij0OUZYC/jDg9wPZK3TKmAK5RGLOhCSbEKmQYql6M4n+rOe+2zMZj1GQg2d/d0sII64ViEVbt0g7kQoxhwUy0s7KwG/UYo5rLMVY0IUYR+6u3xCaM6ac2fkJs/eYpEjdGtVYWudIujoxZ8m2U3zUuJ8lpWo/6mh5YyqNWKq8fGo+qdQyKrF4nGlFyaaG88Kzp2uePdnw+z95hkkDb779iqZt2E+aX379itu7kckLjPluB4kVDweuT9fmrD6j6v+PJddceT4momgUpoH2CRQlpsMcRrIt4Eb6lSLtDP5XmvvNZ/hUuD0MBJ/QReHHEaPvKRmap8/oULiuX8xnda2gtTEoo6u7tl7eeFkCnD6eswfvcK56Mt4fuLm74e27K67ffIXVmueXz1Bn55hH7LWsxLaJFJnGgZQCwqwUhCGHEWOUbCa1LzccDqiSaK1m27dYY3A60ygRw5bbJWNLAS+VnS9gSqZzhm2/qiMVCpcDZcwEbwgxSZKmDGOQIBZSYX93hzMWtiJcIEa1qmpr1oH3R6M6GVURi4qCUsd8ZqPm2UtzCYQKlMyGTSHzzbdvuLu+4vrqjVS/QQbz5dSr2iMUz8jONXivifQ0zRbdbAhJhLOF/CNjI65p6FphHXZdR9PKudMkEmUx30WVOjspc7yny3eu2VXOdEbGnbrWoKoDTPJhTpmwdq7vK4qjFNnoY0cxzeuqnMCpmfFwz3C449vf/JLbmyuu3nxDHEbarufy/JK2aeWh5SiEbbSmaQzn2xZTBkoutJ3hzHa0GQoj4xSY/B7rzojxhPVaIHrPCMToaZ3IkJUU2N3fEn3gsNvRrTpW/Yrz8wtBx/oepWQOMJdI0xixyTIKYyzOKJTJddRG0beOprGYAt5H/BRJea6HVbXkksq9lFkxpvaTc6HrVlhj+Nnv/Zzt+QVPX7zg29/8Gj/uidOew81bDjdX703rfNfx0UFwHkA2xhCKsMhizJQEFAPaYjWsG4emJZvEzWQJKIJSrKDSeROhCNux6RtaZ3DOEnwgWSHaHIaB/WFgv9+LUkqThCZdyRK73YGiNMb1aGNo24a2axljJKZEzJKjaWcxrkFbR8DUG3f2sZMjeE+YPDnaKhtSadRZCcsoa5l3TBGMISto+xbXOBECVpLxDIc9MQea1NGuVqjKOFT13OUsHngCh1qKVmQFKQSUbsRMVwmRZtjfkVSHT4n95DE5Y7LCxQ7jKjlB7DyWZnDKD4Q5xZ9O6QXmLBydKeavhQF6ZPrNx3zzzsiUUgqjoLPw6fMzPv/knBdPeu7f7bne3bI525KK5s3VjqubwDhpMiNRNRTdPDjfUkmddkBk8xPizul7mN/FwxBjFALHrKH4QgqZHKJsAirQFQVeoe4UMXcEDKF4ImDQ5DCRJk2kkC+fgNYY18ramF9VzQPzhUVCYEZIy6x1OUuqfehmKZSc8NOeu7t3vHr9NTdvfk3ftjzZbgVZOK0mS6kZtUcHzzQOhDDJbGgOlBxI04G2adDrDcEHKIX9bkf0HlVSdQPPkAOq2NrXKhCreHSONWGCkhMaaOwxuSFMhACxgI9JRKazkoFmJCAe9vfVo89grUMbA0qk8LIS0feU4uNTUZOwWgkqmU1UataJqWdwCYAKlCGjiblwe3/g9u7A9e2eVdeRMoQQF7KRnyK7+z0pZS7OLshFk3WHtj3adOSqNKO1rTOAFucamqatf1xlhOpqfCyZZTpqkBBzIpX32aG6FHQpdEagx94pYdGXyJQmKSBRNEV64bFEkjLiual1pQwV6iYqKkUKIXdMI/vdW26vX/Ptb/6S3d0tdzc3tbJvOd+e0TZu8VucMXtjFI0x9K1DTwOpZDEkaDS2wJQOhKAYBk2/7sgnbPmCzCiHKTOMe9pG/BQVRYJjKozDSHNo6YYVACH0y97hXCIXS85GJBJzxBrLql1hdI9WFq2MkNeUQq0KowKVEj5IwpE4thDyHASrvdmcIptq0PvsxUtM07Bar2shcs3+JnAYB4gjf+WVoG0cZrZJycJobLQShmJOqDKybTp+/MlTwh0c7jRv9t/ikygDPFmfk3Jk8gfZPIzCrRq61tJYg/cDOlm0trIwDOjGYKoH31/85S+ZfGTwkZubO7bnZ7z8YiUZr4b96Agpcxgm0ArbtjTbLbbfYJoejxLvw+YhvDaFyBS8XPw0EpPYImEtOVvCJBlgUoXOSi+zP1vTbVumW8umtaicuLm7pwktq5TZnl2glPgYhiS9mZxCddeWF/cps/eBm3fvWK8L/dqidCGVyH6/I8QV+/3Eq7dvcU2h6TTnzzoRQm4dfpTbFKVmNakHN6jV4v+Wa9VyOvdWilSApWSyUmR9MuhcIUBJziUpWK8Nl2ctf/DjS/7u3/w9Pnl+zqtv/hKrAs+enXM7Rq4P8O3NBh9WZNWAaavqZKkWUQ/f4FzpyZhHrZ8KKI7BQSkeBAsFnJPZ6IyyCl9nCXVYoaxCO4jllu264cc/3tCcRzyF31xb9qMhRsU5t7RlolWRy/M1m/MzdNtV9/A6FF4SKWaCP9RNXNWNUnrLUiAXtDq5fU6q2xQj3h+4vf6SN6+/5Kuv/5JwGOH8GbbrUdZJ06YeuRR+8Rd/xu3VBa2G6bAjzkSO6MnJk/zIqus4Pz/n9vqaEAKvXr0ihFAtoqQKsNZweb5FUN6RGCahxccoFmXGEVWDT5nD6KEk8eQseRkHyKiKGBgRl9AWpR0peN69fkXbrWjalrZb0a16sYZyDUob7m6vjr3RUn386tKb4cG5wJ/nOZdAOCMnylCsoxhHv9oyTBpzl7m6vScGTwoeH6IMwKeCD5lhjKRc2Z79GeiWomQ/OTt/ynqz4s3bJFZUbUe/3tC0Ld2qw2oj1WkCoxRJK9mUUyKkwDCNHMbDaSuTFRNbPdGQeNZqjJo47G7Z7+8YxgPD7sA8hNDYVob2MWRjKMZQ2o6EwMibzYp13/Pk8pKUM/v9nl/+4s/483/5j/nqV3+OH3ZopWlMw2fPf8SLl5/z85/+COxKHG4rVK9QGKtxNkMYGMcd0e/pty1lHJiGA7/++msKLavVc9bb2TvyuIijD0w5cnP7hmcvntGvN2zWa7abc9pmRUyFYRy5ub7mzZvXOOc4P7/gk5efsN1uuXxyzjjIOMY//Ad/H4Xi8vycv/f3/h7Pnz9HO4fJGmM1623L1BgGrbg+TPhU8MWIgHaWRHG2u0rx6PE5llJTFEXXX/DJ+oKnl8+IhzvC7VsO96/58su/4O//w//Hd7KxT4+P59DURC1X3UJlFI116JDQKROmA5HI7v6Om6sb9ocdse1Ba6wSB+lWO7aqI6TAfnfg3dUNXJxB3zGNE8tsmDZo25CV4f4wsRsCw+CZQlzKZ9d2pJKxxpCtiKkmZciuEXNe5ia33GhGK8l4y8OKKZVCyFmwbCvansYiFVKucJsScktKIkbc9Cu6vmHqHc4IEef67p5VSrJ5ZLUElEUMVjmMNuRcGHYDN3eiqXp7dw+6pWl6chKDXKwl+MQwTNzd7Vj1mqLdMmQcomgh5iK7ilLxQUYHVMksI3FsJnfUAFeYBcyR36/9QcmCpfxbehkFuk5zftbw4x8+5dOX5zw933B75SgWStZ8+eae250nlh5sx9zvkGyZh4SYSsQ5abEx38ISBI/fVIr3HL3zqpAbhQ6O/LaQ94l8u0NtLXpjSZ3DtB39i55mremUgtUK7yGFQhMyxWVS58ldJruyiD5Lm0oRvSdOIze3rximkbth4my9Yd2v+eTl51jXAELaUnOTfH7XSkyT/egZdztU9GxbQzYXnJ9dslptRabvtIQshfvbG6wq9M4SpgMpBkISlwhyJHvPVOAexf3tHSEGbm5uKmQkfSmRgxOvP9FTj9VUOkE66kcGEjEXfEyQE+SMyicVnDJVT9PUKjiRE/ixkPxEmA5Y12BdIzOXxqCNyPhdXV2/t/Go4+Jbel/H/uoxEC56twtbWYyEu65jc3bGfr+vupeJGBKFLIbdyRCTJuUAytE0isZB66BtNFqvybnl7vaN6JIqW9+7w1kheuk5ZNXlp4shFdEW9VH4DKcbh/v/svcfvbauab8f9LvTk0aaYaWdald4wzmc4yPbYOvYEtCgQQeJDk34BqYNLTfo0aGBRJcPAB2EhYSEJUACZByPT36r6q2qHVecYYQn3onG9Ywxx1xr7ar12scCo3Nvrb32nnPEJ9xX+gciOo1k33P79i3B99wf3tEOLeM0iOD+fP1bU6KUATVLg2mDKqu5U2XZLD+jsImygMOhZ+juefv6B/a7W8apJROwWj5vWVcYq7i/fU02BWgReQBzAp9pBYXNEAdSmph8x/32nrv9lmEIlHUlNkS2OFFejut4uxmtqcqGsqzJSYQLlPJYK1037SzjNJJzpu87Xr95xW53zzR1aJVJYeKv/uk/JcbAarniyy++QKF48fwFSck11e1a/BSYBs/ubk/vA0MCPfM0j6jcnISvmXKShC9Jy1rGibPMe8ikbMCWLFaXLNeXfOr69HboqYUlN75VirpwaCa0hzC0jFPPO2v54c1bunGivrjAlE7MQcPEsiy4XC755rtvafc9KUSsdmjt2G37E9jDGIu2JQHN4dAxjZ7ttiX4SIrynHq5wMdIYzU2GvopErSBqibPSCJimhGPCqvAZbnZzxFbEQg54+OEKqBQFuu0zC9mI2EJ5IoQIillqqamXtZMy5LCarz3vL1rWaeENu44BxfHCK1QyqCVxWhDmAK77YE3N3fc7Xbs9zucrVlWKxGntQ5tHGMX6LqB7XZPyg7tGpSR1tMpCM6UBGOKOQg+7MZm9p1LR+LVWTsv54RGkdIckE5BEFAJNZsjz9NB6lpxcVHwq1885cWzNcuqoikLYtbEHHl313K7m0h6AcdgnQM6n8345t0lZwnkSs0mOuoY+E634XlsfAyoURAXEAtNHizph4l840k3Azyv0E8r4vUCtVpQb1aYQoBSq8sGFSM5RrZ3gc5kDqUn1JFgo8jU5ZnLpjRhmuh2W3787tfc7u75/uaW59dPub58yuXmElAzDzHPH+txUzT6wDgM9PsDJkWuFhXaPmG1fkazvJjJ+Q/XYM6Z/e4OqxPUNWEaSDEQ4oRKEZ0T2c+IusnTdi2TFw1MZqqCnf0GjVaMUy9EcCuO70pJSz7nTMqRKUuSmFAQ/Sy/Ec7mobOTgp7bczkTk8zamRHDap4JPlRvGrRmuzs8Qh8/wjXNSdvDdfrQ8pZ5kZ5BR/NsOiWU1VRNzUWM3L57yziAn6IkpESMzfP4QoMKaBMpCqhLqCtFXemZJqEo3ULamkpjizkIOo1BKDxJizA9SXxFw8xXG6eJcXrAEijAEtFxwA9b3rz8hsPhnjf3rxnjSEiBHGZ1pwwah1IWTEnWRig4RUXdNCyWC/TnVzjTUJiIH3ccdu949eM37He3xDhhNVhnKCsJQKjM61ffSKWu9anNqDDEdJo8UjqN1plh7Hnz9g3vbm9YXn9G0ziWzZrCFdLSPlvWCEjLGUddNafqbxhHQkys1wVVU7MsHOM4MsxV4X6/QymYxg6nMtFP/PN//I/oh4GqrvnLv/hbFK7k6fUzCVpk3r1+K1iGmLl9/ZbDOHKYPGW9mK3hHBqDUeKUk1IWtPKsFhPy8bpOgmiOgRQVm8WGarHhOLb4U+tvwKaQ19RaiyJ4zjhluSgXXNRL7n7zGw7TwE28wT19wmK55PLFF7OQL3xWwy9evOBf/8u/5K/+s3/Ej998x3/8//6P+PZ3L/lGv2Lfd1hrZb5XWPq245vv3pKTDN9DUJA0mkxSBo+iC6LK76dInxLVs+eUzYKb3/41CvC3d4TDFltabLOgipk0PT4olatYVAtSXRHiREwBrYSKYbU4e6ecZGSepD13eXVJ/SvDdPkU9WbLzc07fnj5HV//7AuUsaLHmAVF2FTSO88pc3Nzy83dHb/+wx/45s0r7vuWiYjXGu0sZQyYxmHKBe225a5rudseMOWKBk1RNuiyIBxbdjGIKWhMM0/nYQmXRzH77kgSnvIR0U7KQrw9ZlsKKZIVx8dLpZnIFEXFcrHi2dMX5BjY3r/l9ZsfuNt13O16/vB9y90hSdsMBBxwuvjyAwgwy+UviVTipMl8VhbmnM5aZKKGcx5iBgvjFKl/GKj+8Rb1aiR0nnBYEMeM/jM7S4L1mCmitcE58RxTVlM0ljFBSoppyrKhzjDrlITnZJoFhYbL3QaUZww7ajOB33Pz+g0XT55jXIVWMsM2J2SjrJgiOSasuWS1WLPeKJrNU+pmRb1cS1LEw+ajFKybivWioiodbRCFE42ofVgF8Uz+TitRVKlrCTwKRY4S5GKInPCJ2hCPmTKzu3qGwGxQq8REV8mFPQdJIYYTI8p7hHI9n4t5QnpSFkLJnB6Z48Wc6fbvuS1wlL5TnFziH6U5D52C04w1ZwHYVRWbVcXkoLCBy6uKqswsa8c0TkJ6N5nVsmG5rLl6sqFpKtbrBVfXl9TNglWj504JXDQFk++Y4p4iFBSxolINR/RznJPArDJp8oQ4Mk4tUxjw0Z9f0QwRYjdw//YNtzdv6Psd3dDJecpQ2nJurauZ/wiJUc5NUjCNFC4Sp8y3f/g1330j2piHw4H94cB++5bJD3P3TaGdo1kuyEbRjx33r/5Ail4CgHai6MIsS2c01pWUdYPShmEYCQHWy2surz6nWVwBpSSk7/EfQww441guVzTNAmUM97f3rDZrrLMMYw/TKE7vVUVTL9isL/DTSIqB5Edub95x++4tKQSs1jjr+N1vf4fKmi8//5qiFDWn3/7177DasKoXjEPHYbfjN99+x3JzSVFW9GNgOLR0hz1C3RA1m6REsSzxINZQFA3OVZRlw/Wy4N3b13xKAIS/QRCULFEyxaj0XCVFkjVgLKZZoouC3FS4iyvK9Qq7XIuQrwIq0Is1bnXBZ1/9DJJi+U9/zfZwoOt6xhgks8wZP3rGcWIcJ8CRlSEpgzYKMNi6QZc1QRvavmcaJEupqgp3ecVh9QbGERU9LnpsmMijZKzqvWBhjTh3RyBmEezlBDKR75yTkjtknr1Za2iWDVXW7G73TCRaH+hDoJ88u66lzokiJbK1EqR84PW7G97e3PDDm1ds+5YheVRT0mfPXb+jCYpCF5Qh048T/TAwThNJ2PGoI80iiwJIPnMJ+NCf7ihVdazGToM4mKd0R0i+BCLhZZ1D1fX8lGfXFzy7vmBRlYztLfvdPa/evuN2N3K3nzj04IOAWx5aX+mB+J313LbNM3VDUdXFXCXP6NZZNWLyce5YH9ujjwf3ISZiiMRJUI8zpIY0JXIXyF4RA0zJi1ppNmDEE02hCGR8PiJqg/yZrWFSjFIBWYsqK5pmTYie0Xc4VQif1MfT7GzuJp/xqI/KMHMF3azRTmNLR72SG9tY956btyxnFM4IsOFYLR2rKI207TXSWjdzFXo82vM7n1qNEsyEbKxOCY4c35AyYQ5iR4CVnp97BCTEo1OMemzBdXSDVFk8OrM6+j1IgPMxM42PAQknEfdjn5GH13xU8Z/RlpQCZw2lNVKvzgLcdS0myc44/FSRkiRyy1XNclWzXq0ErVjVlEWFswWQSckTY6KpHPVcSWkd0SkQfRIJOqMkC5yvX/EAFX7psUN1viLiNYirsIs1hZVRTAiBlDPLphFJQ+mtIty5JPfCfIvUVUlZWKaxx4dA13b0w8Aw9HNwE2NZrQyuKHFFBUpsheS69eQUUSrJOyktQMCT7q5Bm4KidJSVRmvHYnFFWa0wpuBjItNHkJxwJw0xZfb7HZmEnyY2FxcY41BGCwhIZbKBwhVgLdlq7lOma1tAOITWGtq2Zbvd0nYtKVcoPZPhEbEFpSDFyH63w7iSjCb4wG53x9tXPzL2PSEGAYUZ6ShkJbKIOWXKekPTrNlcPKfSizl2fNr65CB41GNU+mi7opiGQKc0zhjqn/2M0ihYNrjNBlOUgMzBrNLs0sSbXvHNbc/Pv/4zFptrfvvNN/zVb3/D7esD9aJiUZasyorDfkf0k0iM6UxSQOHIWpONYfHkOdXVhlBUbN+8o9/tGXxg2SxYP3vGeHtL3N6R729YKo+LA9v7gVSUMIxncwkB/FhniT4c926YSexJJclak2inmjkJQEO9qjFFwf03LwnWEouSAcX9NPK7lz+wXi6pq5qVH+mHicOh5Z//1a95c/uOP7z8nurpBeV6yebzZ0zTxHfbVzSDo55g0wzc7rbc77YM40hSYAonnEqtJUufN7GyKDDOYt37p1IEBoTwPiu16GM7Ugj6ao5/J0AMnDbWrGbdQav41/7Or/izr5+yqg3vXt7y3Xff8J/9s7/mMGh6X9BNS0I6snqObdmHnV58EKVz1jQFTVPy2WcXFE7AKOMoM9/9oefmfmKaIkf+v8pnF3OGMASmUpBu9qogace4M0SroYvkgyaUma72FDlgsyVFLZJ/UdH5kZA0IUEceoLrGMeBohCYPDmh58To4upL6mrFumoIHnK2kM1pRsFjJ5q5wpZAqI3h+vnn2KqiaGqZAx6PzEeioCGJXq6WYycta0QMAUg+SqfEOUIsUCoQglRDOaXT4VYzWjCmRJzCGWVBM/rI5CNTPELpHZVVGJXRiEN9OkHTOVWeJ63Q+c/D6ZDgl2dE5zgF2q5/BE0/CkGcZoDzAftA1Gpuu6LBaUVTGJrCMA4dwQdiSiw3a+omCt8yO8BgjWOxLGmags26orCOsiixpiQnyzRFvB+J0XNxUbFe1lxsFN9+fyuJ5t5TrSqsMVJdzN6I3otFlff+URV+uqZNiVusuSgcqydPCX6k71q6tiOGwLPrJw+Sg0K8IOaIntVRsh8wOmB0ZN/e46eO3faGafL4EESgw9ZESqwtaZoVZbMCXZBTwpVLlJe2uSTrcs0W9RLjKky5pK5XuKKiqpc4Wwiit1ijTYk2JY97LA/XsNJaJPSMYZx6Xv74PWaezf6dv/OvsFhuqFwtwuJDoM0dm82Sui65uL7g5vUr+r5HKQmAVVXR9fNc8u6OxJqqKlmsllilca7AjQVoxX6/p1lfUClo6oofux3f/eHXvHz5A9M0kWISrNaRfRbFd7VePuXqyed8rUrWlf5Q8PyPrE8Ogkod9STB+0iYPHlKHLQmGUNab0QZxVmUsiKTRZLsMyvGfsJPd7S958frJSpM+KsL6q++5HK1ZIFCjSNT13Kz3dENA3q5ZHn9jGKxwq43GCuQ3cuLJxR1QblypJcviTmzqApq5yhNweb5NdV1w0W+5rPnlyTtmNotprQE9/hizrO0lEZjsyVnsaIxVrhiMUWECWVw1qK0IqWAj4GQAjjN8uqCL+uKL756Tl067sLI9n5CAeGHb+n6nkPbsW8PRJN4/osvuP7ZZzSXa8rNhtAdiF2LPhS48hJVLOjblsP+nhBG0BlTGrJOs1pMOLUx9EeqQIDLdcmT6wVFUbLbdYyjOAXEmEizlJX00gPWzgCFM5NNW1ieX6/4+ZdX/K1fvuBy6bh9+5pvvvmeb75/Rb28RlWGMlmK3hIicvOaEqUy4zSe6pRhGNAGilLzxZdrFouKsjyqRCSwmaADRnnWC8dUaA6HQeThEJHg4+pb6Ewma4VeOIxVdGuNGhMmacY3I04n+o3GlEpmijnhgyZ4RRedMGEUhNjjfcc0DgzOoRQsmgXKObSxLFZXVNWCqlmRI3M70eLKWZzgBOM+qmbIzNWWBRghZQucW2g5GU50lnPemULR1AWrRU25WNB3B2nRDp4pJnRKOK0pnKUqS9phmE1GBaiVcsac1VUpCQVG1FrESf3Q9nT9xDBOjF6UO5qm4nq9oHRmNluVGViMUY54zrPotMiiHQPgKWfKSoQWlNSrRSXyYydcCw+V4GkcOD/vBEY+BooEzmoKa1jVBWVhMFrATCloVC65vFiTc8KHEaMrjC4oigZrM8ZljNMobUjKMUziOF4oI1zBKDqjWkWcm1vx2bDdRtowYgpNVQodIoTIMHimUbQtj23G86WVQhuHY4l1NUWZKCuPMlvGYaRYPDlZMiUk28xoTDQzdWVPVQbKwtO0NfnVS373+z8wjH5GQkI3enyMfP75C66un/Pl5z/H6BIyXF59xdFFIiVmvqfBuBqMYAhcUWKME4zF7N6BcjxwM8/ms/P/5rkCjhFub25ou47usGccBnKGMHk2F9dcXj3j6vKKsiypqkrs9PrIu7Hl5uaG7XbL5eUFy9WKZ599zm7X0nU9+/2eqqmomoovv/oKqw2FNpSLBUXTMMSMdiVo4au++OxzcvQ8fSbFgvceNc8I1Tw+yRnKZs1idcHV06esVg1+PHw0jn1s/Y1mgsdWSQxBDF+jkGiHmHHLWloKWpoAJJlD5Ln11Hvh8E1hwqcepzNjWcBqQ6kcVUz43T1T29JOnjFm7HLF8ukzmssrzOYC44TTs1xcoi1gPNmK5mNjHaWxOK1pVkvWtuKzMnHdVIxTpogBiESdHmXvIUswizFBVKg08wmzzCDFi0xaoimCSlkG3zGjUsRWJbV16NWa9fMnWAPd7T1hFG3RQ3ugGwe6fkBZRVGWbK7WXD2/pl4v0VXNaDPegqtWOLPBVY1ko8NAzgFllMi96QdgwRFowonT9rCUgquLhmdPVtRlhU6Rg8pMQeGnhM9JWlqzRFFRiEhBnjXGlVIslyWfPd/wl3/2BU8uagye169uePP2lnc3e4xrKK3FZEOMCT0bJJeFwM0Vfr5mEtEntAFnMk2lqUtNin6enQqQJEUPKeCsVFqaWcrsnJ+VIYzgRzHyjYUFq/FNxh4SeoTYRkKv8RGiEmBADpGQDCEpfDIzXzKT0kSM44mjZ60lRo8xUuIVZU2yBcYWJ/CQDwk167YepdYePt5cBVor6kauEP6ZOgbAM/7TOalcQWEtRVFQVeXD5qlmUeAQpTKY9WCl7ZpP73lsv85n71SNJsSNwgdBGo+Tx4fIOE6z3FRgUVq0KoSekyIxBUKY24IZYhROnoCH5OMe6XQPyBeNMoaFszyUx8fP976npeKolXbsF5i5bVg5J0onlaCuFXLfiTO9pq5WoDJT7HFmgTUFZbEEHVAqiEg+moTQpYiAzULEzhofw0nhxNkGa4yIeUwzTWd224hJgGchiDXPcX766B5D8AHZFCd6knUZ2wV80GjXYKwEnwcvRoeNMx/RgLE9aAEZJUTAX/A0Gm1kbhYiFOWaxeKai6sXkNw8x8/keb4uQXDuUs2UFkx5Cn5nTec5eXmo8h/PzR4EznMWHuqhbfHjQNce8HO3rGt7hmHCqMxyucRZjbaZnBVDmGjblr7vub7esLm44Pr6msO+x/tJRl9+IqTEarWiMJbCGEIG7Ry/SNBNgckHun7k6skzjFY0y5XohU4jOc9ylEmfvocta4q6plksxYXC/HFVp/P1yUHQT9NsTRQY+5EUIouyxjqLrcr54Mu4P/tMIDAqj0+JkBNGOzDQkfj9/RZyQkXFFBdCbiUw6UDPji47qGqe/uznfPF3/y7LZ89pj1j0nPFRk+OEnzy2WbBKkQtbUjQlzmjckwueLAxfXhXwckecemycmNJIn8Kjds3t/Y6qtoQ2UaoSowzGzmRylRn9JANZ72XGpTPGZhrnqIzj6Ve/YAyR/TRhNzVFYVg9uWQ8dEzDQDkMXOREVInlosYVlqK0jHliSgPDtgOdsY3ll3/37+LUmtwvcf/Jf0AOHq2yqPo3JdrOqL0I1hVkYAqDIOvOuHhaaf7+v/FnfP2LX0HM/PZ3jtu7ln5IbO/u2W89CcmatS0oCgc50R6k76505uuvnvHf+PMX/Jt/7+eM+3e8e/uaf/AP/zG//uuXvL3v2Dy5EJsTbdiNt4RxwseJ0ohTd12aWTw54YxUz2Pref3DK7TR4n84AzhCmMEwWpKo4CNpGgk+E8fx0X0ae0e0BdkaqAuoFanMqDJjBw1BE/vM2AWmphBcUA4EbYhG47IRIIQOpOlA0A3j0MlNkzNtvyArJV0H47DGYItC5mM543wgxDAHjIhGMvBzWoA29sGsea6CZFSYZ2Pq+AGpXDsrEPhSNi9rLPVFTb87MHU9WWkiCh8Ftp+PoWVOZsJZZRnicXvU9MOEDwGtNZ+9eMFyvebV67ds7+958/o1tdNEX7GoHCFOpBRERWSeoc4lDPJW6QSeeaACqtnvs+LpkyvCrJV9XOdb7HE+KXmcJmVDUIrGWJau5BfPL6lKTVKRYTSEYMi5wLkCawvhtKk8z0UrtHIoBBmpP9oqPgABAABJREFUtSUkMcole8ZR0NP9mFgsCqqywYSIVi05eK4vKpq6YNc7dHNJxPD9t7/G2IQrEsPU44OAMI5mu+q973P6jmc/DDGdkg2UaGYeEwbpjQnyNYTEdz/+wJvXv2Pf7xjHkWyXPHl6TVGUpKgpth1d59lc/ZL15ZesL38hrf05eTyN+OcDLWFxnj/OV4Bclx/71B9Zs8CGmRGjN+/est/vCeOAzglDYnd/w253y/ff/57vv7vm8vKCn//8a77+xZ+xWK5p247tbsd2u+PP//yXXF1fsVouMUbjfeD29oZiUYJVhBhYNQsu12sur6/YXF1x+eQZu26gHz27Q880RcbRc9jvGceRtuuIU5J5ZEgcZSCluxIIaWTyLcMwftp35m8QBI8agDEmrDaowlA1BabQgvQjoVI80RhyzuTiOMAXIIfKCpVmMduYiGMgh5nkWVYUdQOLJZvlgmwMZV2Sjah7hJNKvWjjScaiKJcrrNYsVCbrRJxastUEbxgHSN4zpIhxilVlcUmdeMo5Z/76r19yf3cgjgmnJQhKv1nmE977OTOatTQVFE6xbBrWiyX/yl/8LfaHPW9ffke4u6eqLE8uV5jSUNoKU5eEHIk5UNVCkg5EfEiEHGd35ExMirY7YHMm9NJKlPZXQVkXuLqQ+Q9ZLGGYpD0R4sxpPD9bmaFtOWx3jENgv93S7TvGUZQfIFFY4YDF4Jmil7bZNIiDR2n54umaTWOZ+pbvv/ueH1++4tffvOLVbceuDfTpjrKUANp1A5P3TH4SsedSPndVFWhV0rWdiFhHz/328OBAYATW4v2DTc2Rj5lypK4rNPWjamupC5a6oFCKwhQy28oa54TjWVRLnAmYV3vyfiSVFtbN3L704DXoBCYSVUcwPSFOhOAJwTFNE4UriUU4wfaPZrEiqyZdjjxD+DOQlDoy/slHyx2lTs7XKedT8IgpzaCLM63XDMM4sd3t2bWd3MDKsFpvCFNgHCcCihwTaZrwMRKSzJjyXBGcglOSyucoqReD6P1eX12yvtiwWq8oS8fbynHY3ROCpx/EFSAnCYIPXpPnTgdHYNVc26m5lZkyR4k+oxTpvQT8yAM8+8kxl4Us8+KiLrjalFxdCKLybu8J3uA9aGXmOWpgnPpTG1UqsUByahaLZ76e1JywiF7o2PfkHAmTpdYJnSd0PhCmnjHU5PQE0hVQECYrYDAFWg3CmVNewH3mw5HDh0tR17VIs83GvOdV2OkY6jnJKmvKZk0wlmalef55xWq1ARQvf3xDvSgoas3TF1+w3FwSkjohz08XzvzK+SxCn/cFHt778Sn4qbioZ7AdQLNoZnm/dGpFdn3H6IUy0rVbUpoA8bNcLNfEkOi6Fq0V6/WKzeaSi4tLFosl4ziJQfR+j6scYz+wtXfcFAXWiN5ySJl+Cow+0g2BEMCHTNfNprz9gB8n4jy3TfGhjZ+SJ4SB2qQZoPVp69NNdfM8Y0hQWIc1mrIuwEppYtCkmPDDgJ9kI3BUD5ubnQf9yoDP5JCY+gmDwWqpJm2sceOCi9WSZBSuKshKjB5DlNdUmrndJoOdsllSOksTBgYVCb6D6EjeMnYJ7yemHChKxaqxLPLDDC1n+MPvX/H27T0xZJwpZ7RlmoWLBQChlWgOEmVzqArNZu3xV4bl1ecM+Q399HviuKOqDZt1hXXiy1YoR0iekCaME17dOE34+cTlmRSaU+aw36HziO9LIFEWjmrmBrmqJMRjEIyQJnIWvT6jZDZ0XDnDfrvnrrnj0I7c391zOAxMUyZ4sbMprJUN2cvcIaY46wVaamd5uqkpdWZ3d8sf/vAd3/7wit99945dpxi8YtffUVei3t/3ozheeI9YJjqq0lEWBUVhSSni5xb6MHm01iwWNU5ljAbv0ylZMnNwVipTL0qceyzptjAFS1OgVabUBVpZimRFp7DQ2PUCM7bolwNZeWKtyV8rsoskY+YgmGUOSYe2HSFNMgeZbywfvHijKSOI4PxgPqr0MSgIyV46U8e2eSJ5T9YzYOSoxnFEXh69z+bjfUJzkumnCbU/zBmsoiorVusNbduh2u6kzxm8P/HXUoqn5CzmOL92IiU1B8BMiglrLU+uL1mulzQLcf42KvHjD9+LY8AUKQoNKUCOjwns6riRHwOMfGI1lyGR+IDsnA/to6Xgcbs+nwKgzKACZQGXlyWblSWGzJubxDQlQjCUTs9twUD0s4JPEgCTwpLKeT6kFGVpMEahrfBUY8z03UhOiVBadCn3jYotfdsSckNOjSQX2RBDAYUAzrTphTrCERX/8SD4WHIwiyB3VYkz/aNRxcPxU1qhnKOslixWV+hqSVXXXF8/paoE2fjqzZ56aTC24MnzF5SuwuczIXchf84ZgToLag9JCnkGSuUPI95P/fw8CC4WC+qypKpLaUX6ie32nn27l6Rk7BjHjq7dMYwTi8WKul7QtweMUSyXK9brDev1BU3TyFz2sKc9rChKxy5EEaqI6fSdlLaCXUgwRojJEJOWFr4PjOPAOPaEMDFO44ySnQ2TYyD6kctVjff/VaBDS4trSkxVYaIEtKhmuG/IuJwgBEI7EcaRDBRYMcKdJnIlnm2Fc4Q4AonSQs4TWnsWi5K6LKgWlzxbZMacOVQLJgNDGAhJwAfyT0TliMqBVVOxUAVPVcM+RLo88vRySWMSa3patlQmcPHEcvkEppgxDxN6vB/wQTa6KcgNXbgZHGMsT6+vWNQ1m9WaFKXK9aGlLGuWTS3O3j4wjoHRd0we3r51LJZLiqIUmHWSFpo40ifGICCVnMHM72W1Yb9vRQBZO5bLirBZ4q/WbC7WLNcrrCtls+M4V5KdJMQg/frjHCZn/vN/9Fuevempyoq7bUffj7T9nrrQNKWhtg1FWVA9XWG0JobIfrvj9u073r75nv/z3csZVBF4d7uj95EuO0KU+UVp5plTSnIxJmlT+RDIOaK2Ce9HqlKoEHVdocyCQ3uYq6GEYx4pKZGzKoqKZy+eYYxhGkeKsqLdbx/xyC6bgqtlhSpAB0WcEiEoxlLmj/evbihv9qx+/46ftXtUqXn35yP5qxXqumHdOLIV4sUw3OImDeW7ef4L+l5DmoETDdjswBWzuv981WQ59j4GdIxYl/D3B1LX4++26M0KvVxQPbs+gUBCkhvV+8A0TUzT45tUG4VzhkzJolnSNA0Xl5copVit1jit6PuO7W7LYWiJQRRfjoHQBwmsKSaUkjntOAaaZsFyueTzL54R4ogPOxbNivW65Ouvn/PunbgxFGUhLUatxJNTzQD/WVicWa3o5DowH4vtdscUE34IfI5B2fdnZx8LHnM5lzKORG0jq2bicHhH20ZeveowdoNSirHfIULinqKE4BOHfaAsBfTlnOVw6Og6udaXyyXXT64pnCSsvXU4V+GsJcQdhVFYUwMtCk82B3aHNwxh4Prqmcz1isyURmLMwAGFRn/ke5zmsmfBRM0J83kAVI9kA+dWtjasLq9ZXKxn5RPZbxQWlwLPX/yclCNKg3WAnb0YzQMW4JRNHPui+dhylrnrTwW688/6uEpnDihpBtpYnHXUi8V8yjJPnj7jcNiz3d/z+pWgQPu+582PP6CNoSjLk5t9UZZobRmGyH53YLu9R+vMalVTGcN+v6c9tGzvtxzavQS0HHmomqVASFlUmI6JZOCIt80noN/Yd6gkLdurX/0FxU8kLR9bn2yqu6gNq0bmNTqI0apzkGcwjNWGSBa1FWUlmJTqBJSXjD4So2zgKLBaqoQ49661yjiT0VWByTA6S1RyEAygcxSknEkYnXAOFtrQoFB+YlE5ame5XlVUeKrgSYUiKtA2UuSReK6arhRXVxs2F7VYEmkhVRfOUBSWsnBcrDY0VcVquRR+WvT0Y8TZgrK0Qmgls1g0lGmDMYmUNcMQmCZx2hi9Z5xGBj8RYmSc/e0ymaJyOCfI08p0LKs1TzcNzkhmW1YFCRhGj+m9cI6U3DRHj1qjLOZMAzCTabuJ/WHAT4mxH5mGkaHds3IN69px2TiqylEvKmlJjjDeT/jDlvu37zjs7oiAT4khaiKW6MypFZOzBN9+zEzRz16phhzmQb1K5BwYRystZSWcxGMF471krEFrYgo4Z6kqJ8mHVuKDx1HS7GE5pyhKjXaKmGT+FUJmUgKjz9NICgOFVrTGobLidheY3g5knyk/W6ArQ9aKm3cHASCkNxTUWCUdDudE5NdYe0J+ipoJnFLsGTFJFneRsD8Q9x3hbofKGRMTxdXFzH3l5CV4/Du+57Ywji2FO8aGkpgm+v7ANA3E6LFa5pLiESkUiBDiCaUqLWRpgzK3XY+zQJk1FuiYwM8bq9XUdSVJqdLUdfOAuItz5ydmUp4BTkkJJ4xZiH3eeNt+mDdN8QL9FDzCCcyjxCLKkFDZ07WB9hAZh4ArPNoYUlSkLKay2hhCyASfKMs8t0ATxiS0joxjjzGarq2xDnIWybgUA6J/L92IccgUTrSE6yrTh5ExdGLKHRU2ikxYDJmUpBX6PvjsnD4iW4l69PfDFnPMnB5+LuL1CqUdBjOPZxSgIRu00aw3V6TkYZ5JHh/ywMPlvS7n8XPMhcmpOvyw9/mnguP548QWyc7o8SwyhIA2WtSL2hYy0j3xnpyjCL2XBfVsrxR8oOs62vZAVTumYWAaR4aup2tbEQjY7/B+wofp4eMqQfWGGB9ccpSiqGu0s3OrLkOOtPt7DKJiJvfwp2M+P+2RCp6uLZuNZjgMcxBUFLUjWUt0joQV4WlToHUhqitWUXhL8BltLd5HumEgZlGfN1oqG3KSiy4FdPBCRZjJqHY+7cIhjugYqLWiNIplARfa4mJgajsunz5lc33FxVWFTiNq8JihwI+JEEZyv8N3DzxBpeDP//wrnjxdSSZZ1RTO4bSmrkrqspxbv4bCFXPPeWLfB8gOox1Dv0frxLPnT7DlBSnJhXFoRcC26wbarmO3b7m52zJMAlOfgrQ2FwtHWTnK0rEsV3z29AWb+hJnM3VpqJuSafLc3e1QMVJUjrJ2lKWVeZXVOFNRVPkhq8swTJlDF+jagTRF/DTSb++orgo+u6h4ftFQVSVlXZKipz1MvOt3HN6+4vV3PzKhpQIqS8rNc7SrIBccDWFSzgyjIH6PqDFjDCrKBhkDdO1RwmlGSCpmInYmhsQwdCjEydwsahaLmhQmIWHnzDR6hr572GgAV2vKWmM0dEFafuMU6f3ApAdgBKtprmvermpCyvzQRe6/bQmvOy4XjiZbrDZ88/tbgu/YvSmpzEKkx4iQgjhoO0uq4im7P7aK0ozyDCEKilV5hps74vZAut+ThxHdDlSfPxOxbKWk3ZqSOBLM7Zvjyjlzv31DZklZVJgxE9OADwNDP+LHwGqxFjCNUnOAi4xTOAEf4hxXBDQhKFZpT2uKsqCoCmwCV2hSUmhjKcuKoigxJrPZXM3KQ4mu7RhHzzCMUuXHJLP++R9Umit4Rdv1KDRlWRFTEPPqP7HyTMZVgDMGTSJOHbv7if0hM46axCRefsnIzDZOGFOSojiRO2tny55E1QDKcHvTkg5CG9hc1CgtCOBpSqRoMLVnDIHYeS5WhsoaVisY08gUI3/49oB1cqyynoiz8TX2QzeTI1r409ZZADzt8A9KPFLBgfTeNdZYnl4/JyPWRIr4qN35p47u8f2Ok92PUageguP780J1VsWe/5E2rtOCA1itltRVxX6/R2vD3f0tw9ATgyc7oQWtN2vKsmK3H4QjeH9LVVu6vmUYlnRtS9e29F1L37dM00jfdyeh7Jwz4zgwDINoOyuxZnr+xZcs3BI1252lpNhv7ymspSkuqcpyvsY+bX1yuFxWhqfrinJTUyuLVZpkDUMK9DEwhlaquyeOsjQyM7TMUGZNzJlhCOx2I12viFED1SnLKPNAFSK1LjEErNIEW5CbkuQcw3DA2UDhElfLilJDkRONBjzcbBOLSrNqLDkNxDTJBVdWwnm5P9BkzeDPEg0Uz55e8dkXl4xjLxdOziQfcFZUJWKKaCUtIecUxlmCKjG6xJqazWbJ/nDgbhvo9numaWR7f+DNmxt2uwN93+FDYAqB3b6bxXgTYQZX7LYHjFVYp6mLLWPruayXlFOHNhnjHK/f3HJ4c0dvDUXtWCwrPvvsOcvFguvLS8xsMXW++tFjhxGVA3VZ0DQrPv/y7/F0oah0xrU/YIaAaT1GZZpx4rPqjuGZZ6lKbtKKXlV0usHWK5SyiI+k8JKEcxgJMUpVqtXMpRKnNT0jLI1+aMvEnGf+qEi6HYWrV6uGpilxTrHb3ku7cCa7tvvdGR0AYvAEP5GUYpoUU8ikPNFPA4cw0KwgNQ6zWNDfRw7txPc3W7ZTJCr466XjelNyvSlQ3/fEEHjbveLti7e4sqJaVAzjgA+esm5IMWNNKW1No2BuaccQSVl0D3VITNuWsO+EQtP1qJQY2x5bZ1QhSiIxprk17gln6FCl4NmLBRcXAvsXFOkgIvQ+40Pm9e/fcji03N9v6Yf+pFoDorqjjDq1i8ZpIgaRmHryVALm73//nVjYFJa//bf+DjfFPT9+f8vNzZ720HF/Jy7dPoQTKVnAOzPVXc/OGRq5v52lsI71dTOjQ2uy33Lo86N9Op9sZo/b7lxtzn6WVWkxSjGNkW7fM/SgKFFEtEpYV0kCMUUUmqooWJQlqEgYW9rdgDMVq3rN8otnTDNh/+2bG5SKVIVimjqGFNje7Cn0QKUH2t2BstKsOk/rF7RjAdHQ9nvubxPLTYMrNE21lM+SHwf3kzn133hJ0iK3gaCj9WnDVqfWqYw7gGxmW7b5eWcJ4fHVHk1cFY9+8n78y+8FU6Uef4fjzPqc+6p1mp8rerTyM816vRbptKbh7bs37Pc73rx5jVLiBD8Mg5jhFoamKdnvNW/evqZwBX6Q5GoYRcjg+CfleOyEMvSDBMFxQBR0DEorxqGbkfrmdC/mJBrKoq0q5+dT1ycGQUXMmqzFDcJpg1MGrxUqCBRNlOsFWViWFmcVlVXUhaV0hpAjg1NYoDKKEDQ5OVEx0Jo8ehwaG5RoMirFujCY2oAzDDnhbKZ0mas6C6giBkplSSrTl4bKQWESiYmYPTFHotJENFNUqCnRj+HRxVsUxzZcmiWSIlGOr7QeY565Qx41c7S0VjhnKQsZGg9jT8qB3X5H18mGcne3Y79vCcGfxMfzfCHFmMWMNEEISRD7PpGjVInBe2rEjVtby3gY2Q6e+xhxpaFelGhd0K8CKjsuL4zQDM6u5zQP0ZWSdljVVFw+uaLWI0oNDwhTPVE3Dqs1ZQnNsmQ5LenCkphLxlzPrgmK6NMDyGOGySulqOpStDmVmuH1aW4pinvHSXEkxRlsIRVASvI7a6UllFJkGHvGYWQcR1CKYeg4/2IxzrYqSuED+JhnnpjM8SwGo0Qn1BOYUmKcgsCqs+LdzYANsAwKN4rV1dANtPuWw74VMnuUjN17ORcxxpNBsp4rgJwTcZpIUyCPAd/2xH5Ea0WIcgP6YQBrMLN0XowSAGMMYm90vLsUlJWmrBRKZfwkFIxx6glB4yfY7rfc3+95d3Mr4CY4wfaVkiTq+LnGcZqPU5zfV3iCZVFgtKOqljjTMw6evhtp2154cXNwfqhS5XX1bNRaWDGIXTaFaPyWBXVdza3siqZStJ2Hs431fMueQzYnkAiicZvJhHlEEIPI7KXkUdGQsgifp5mf7IyhKrUcG++lXWozzinKcolSs91OP6DwmGykJZoDwY8oPWHshElBkJb3HUOOjMGRQkOYItMYSY0T0nm2s3P1T+2NH2IxP6iu3n+8ymcPmed37z3zAaErbVJ1nCWet0M/MnFVZ7Xm+wGO/FCJfrQ4fHRvP+AmJKDo03OkUmRGwAqlIkaPc4aua08qRcPQC8f25PeUORz23N3doHKmcOUsMzcDW1IkpTAnzczC6HFua+e5+o6M48CRHZBmtLVWIrVXldUHzjN/an1aEMzw/cHQ3RkWJlESMWQ8mTFGxhCxFDhnqLzFmITVsHRQuUxpI9l4mSvkElMWou6QNc4prM7o0pAPmTgMZL+nKAueXaxYXhuK2pA6jdJCjyhMDzGQ0oTRFdlZyhdXFLXC5j0oQYSOfmL0mjEpsAu2h467+5bzBG4cesa+BxIpTKQcMW6WJ5tPeiTSTz1MclmG6KlKS9MsWG9WdENLCCO/+c2vubkVHp5Y2xguLi8oa0dRObaHA203cHOzIyU9z2+qWXYKSudYrS/57MVz1PYW30FZLrCTQ/uJfr9j347k25Z3b3vKwrFZN/zdv/MXVGXzKLjXhWG1KKnrmvW6pqlLVhcXmDQxxZF3XlOXiqu141d/+0tSivx2+E/Yjwfu80DXzVWWz0IOTwJ0OWojHuXzCqv41c9/hissKXru7u6YponlspEKhczQ9zOyWGbBxmia2onsVwyEMNL1ER8muu7ANHkRyk3y3PPs1Q+JsZCssB09QwqoeuCiMVyxpBocbkhk3zLdd4TWs4iGTVOhrOGHtyN5r1neF1zXa2pt+M4obm/usLbgVz//GrMocFUl86NpYvQTGZFqU1oLICkmhjdvSYeOtDswvr0l+4ipa3yO5Ohpb+4oc6aw9gQxH3xHCBPej2fbFcQ0Mnnwk3RMgk8MQ6DvE8OQOAwTh6Fn3/a0nVRqWs8hRSEGq1qk7k5kdwXTLK22Wm2oyoqqrEkRDoeOl69e0fdy7Qo4SVE5mYm6OTlcLqVtvl4v2SxLFpXjcl1T16U4fDt7ap2FEHj99ghkOlap+gzEcdZem21/Yk5MPtG1gUwgq8gwTmgfUYzk1HMMHNu7G8KiZLXM+O5WkIKDZ+wV06TZbL7EWFFIySES4sTr7ZbVqmaxKFhfVRgVMTnTmCUpZvZ3gcM0MgQYB7lvC2swRHLIDO2ELjJxeszrFFuqfHKgP28bfrgejJiPYe28QfpwvEDEvJVYWZ0elR5KuPMS8Dwyn0W189d+QCDLOtHLzr7H+cc+ckEf/oh7gzrOJucAeHw7rQ1lqXj2/BlX15csl0tubm7Y73a8fv2aarcHZTi0O7r+wN3dO/qu5dXLH3l6/ZSqqqiqiphmwfKxO714TB6lE9YJ/yhnCHHi/u4dUi3LCEgrxWq1YrNec319TVmUdH37E+fiw/VJQTAraO01yjxjmzwmiQknBiJiQqlwqAxmjGgVMDrTTBqnwKqMp5/PiSYNgiS1cWRZZhqb2JgePRzQ0x5HEJTT1JJuR6KFIg9gMspmAh6jDYWr8EGL+rsRuHrKnt3NlnGKDGPEumu0sgy+Y3sI3G1nQep5+Tn7FSd2i8Lgg5e5lpLBq0aMewvn5sGzw9kKaypubt9xc/eG7eEGVygWi4LoFYtaVNg/++wpZVNQVJbt/sBuf6AsDNMoihQhBIYpMIXEZrnh+uIJz59+xt0w0HU9991IPwVSTtRVyRQUo0/kGCFprM4UzlAW5uFiVkcXCdHPiN4zqMzd7S0KUYtJPlMX0OfEi1Zg8PejJRcbFpcXFGsrwJMklJQYAvvdHu9Fj1LkwGTDvNhs0BqmaWA7c7RE3kiCYIxB2sl2bpuqzDQNTNOID55h9LOFjzn1/4MX1vWxqjreyWmMxCEQlah5JKWJuUT5JCjlFClRVKUhLSu0dsRxRK0WhLLg7b5FVzV6uUJtljJj6yfGcaTb7djfb2nI0vqc24vWOvIikVMpG1MGZQ3KGrFdevOOuO9QWeGcw89ZbXtzRzSaXJeMw0AIE5PvZ2DIQ+WeM9zdHujajvYw0rWinzoMgXHKTD4zjJlhmMTP08CRwnBcIUZUPMYbmcsarWm7lt2+5Gtn2WxWbNYbXr95ybubV2RGnjxbAQvhddaOsnDUtYC1ytJRNaUkt1VB6YwIW89m19hM1HG+r2cx5feqj2Pd937j8DhyGn1i0olgOXVMQgik0M/I8fL0+qiANRCCGBorZei6HcFbcnQE39N3e9qu5eJiKeL3KPaHjrY7UG4nqiLQlJ5ByXUtsHyx4um7HSiHUo53bweOep+LTUXfjh+fxp0C4EN19tGlHgLgqYI7j33npdm58P3Zy557yB+DglyMc2B6/z3zCb4jb3X+Fh/8x/Ep+VTJPXL3OI40YkTPec1DQBRVHOcUV1dXGGNo6obD4cAwjihj2e93tN1BrLzGgRQi77JUk0VRMEwyTxSgIXMleJwNHpV7jt95RttmPYvwS6WcYqDvOw4HuZc+dX3yTHB0Fyh3jR9HyAGVE07NDtTakinkw8YBlSeMSjTaiOt8SozxaFyqSd2ACp4itFxVkXUZqcoB5ztMGCi0EPCV70mjJxLRNkOhwGmmPJBdRemWhKDwKZFsQOcAMbPb7pgm0YvcXIrclA+Kfsx0/WO0YUyRmCMKh1EWVGaYdS+j0ugkgA6DZFFaK7KxaCUCvrd3N9xvbzkMO8rakKkhOq4vL1ivNvzsqxcUtcNVhovDgvttQ/QTXT/iJ6k0Du1ENwQuNxdcba642Fxx//IHfFLspwmfhDfX1BXGS1bkrKJwhqqylKXBuccg7mMAUkRBbaWAH8cTLNr7SOk1XiVuDhljFIdQgCuorGPtKo5b2NHCxBpBeh6DoCBoLYtmgcwCk7RFEbmt4415VPvX2mCsyHoNc8vTey8O5/PNFlMQPcyUQekzAWhZyWfSmEgqEYwhKE2MBjV58uQx5nhsLGVtUERylQlNzVRXmJDRVQ11TVqvhWAeDyLp1/cctjsxVSssKXjyMeAbOxdXQmlBaZS1gpC930HnRdYsNjMtBvrtllwX6M2ScRpO7VCtH6Pzcs607cgwZLb3LfvdwDQG+j7gg0gTxhkqjkJMn9ODli/wiCcqhZbCWCPBvZcZSlUXLFc1L199y/5wh3OZzcVyHgkUrBYVdV2yaErhuTpLURrM7PxxLjKBVkSdyVkQsiklyIr4Qd/wI0FBMWuGynXoTRb5snR094j4UfiOzs1tsCiuAVPh8JO4MSg00yQdJo0lJc8wtNzevKFpxJYNremHgRBGLANNnUlLUClijKKq3Yx2zYzDAKpAaUfbjUI9Ch6f1oTp8bhhPhJnf/+xFuh7X//YVvyJw/Po9X+ibSk/P3//sxd+9Dk/DN3HROljb3+sAFNKj4LfkQ+ZZnF1rQQhCmruSEiCWy5XaK0py5Ku78l9D1rTtvuTO0aInhgiwYeTy0TKcZ6zhtPn+Njnmgd+8r5K7kc1V7w+CChxt9/Rtv/CtUMV5cXnVE9/gZo8ZBl3l5Z5gB6ZQoIcMdmD71A54AqNTQEdA2YbsDlRGaiagdpmni8NT1ewrmAZLLGr8Ac9W5BAmRMqjKjg6WNALxpoCu77BMqz7XvaQZyf+3TLZi1tj8NhxJqK1eKCzWrD6DPKvOP62aXIj531jKuioq4anHWkOGc6s5sxWQbzWoMyIsuTkwIMu92W7bbj9du3TKHD6MQvfv4VhSuxqqYqK0pXsF6WsmETqesV11cLPn9xxdhPAnFHsdsPtK3n6y//jE1TM04dt4ctN92e4DTNwrFyjuZiI3JcRlPXBYXT1I3l8nJF1561azJ040TZ98QwiX6lFlRhVZYURYEPUWZyyfPbb99SlgXN+jNyP6JD4quvvhBXBaV49fIVvrBcrlcyJ5sTmrIsKUsh0oYQMKZmtbrA2kLUY+bNBaSvP8WBHOR66fqRadaT9bN7QU5JbH2Yb90cZ97Q+Q2hGSPcDRO3QTMqi65XmKBwCdRFCUaoOVErRu15EzuyT2SbWV9cklLiZdvKRlw4NouGVVFQWMuv//r3rO7uWF9d8uR6LTPf7sDUD1T1guVaNGyVNozW4W2BV5rQjSgUsS7xKROA9tVbKhUZyox2IqOmZkmS88xfKcX11VPK0rCoB/aLjmHw7Pc9XT/QTxP9NImzRmJGYM4SV3p2lCcL33QWfzdG45wTEWmXePnjb/HjDdu7NW17x3qd+G/9G39G1RRS0VuFM0ZmuKc5UEabOLfoMlHIDOLsHR8UcyT5FVucdhgfjRsez5fOfq4SWSlisnTdxNvDwHAYmKbENGTMPOdcNAXT6Bmjp7ANKRje/NjiSvFyvLq6lhluUqwXBYWNhKlhe/cOWziePH9O1dSklPHjAR9aXt/v6NqWGAKkxJOna8rSMY49h/2OwyFg3APAYvID3p97ZP7/z3qUjCFVeAjhFACVejCQloT0qKCkUFlLBw0z00ikvbpqltRlzfbu99ze3fLm7Rvadk+OAauUMEHyUbs3Mx09XmfqzU/STDjG+YeG8vHxXSd6pTfvbvjx5Y9Mfvpk4NKn+wkCegY3MMOyc8gytA6B7MNs6RLI44BG/NxKJlyeaIqeUiWWJtLoPbXzPFtHFqWntAETPNlkytKdiOA5BowzKCOzkgQiy+NBackOjRPgjdMr1quapi7oqkBZNGzWG6wBHyJNbSjrgqrSj9zKrTZYLa3EkAS0oOe5zxH4oWZjPR89cPRfG2V+aBWVLamMpq4XOOtwuprlhsTGZSY5Yma+ma4LrJbZTc6iUNFUieXSYUym9y261DTrmuf2KbZ0mMJRLCq0NWhrcEafeFJK5UeQ++PVkRC1nZTT3IZ8ME4dvccahbWOqAxZiyODwpNiYOg7tJbMbxg6Qgg4++Bgb4xUCNYaxjERU5gDn1Qg+ONM4eTxI12AJCCHoxydUuAKdwI+5JM683tZ7rx6LzSBdoxMWeFJZCYKDcqUxHJNyAHGnv0wcug9+xAosmysalan30+e1HXYVLFaLFApEsfIvhVvyyFFqtKKe0JKaGXwo3D2jCvErHR7YLzfMnY90Xu5Vrw4P4QsKiR6HCnCJBST8xv8vRvUOUM9eywWhWWaPHVT0PYF/TDSTpPw9qJUSwpE19RakYszwq10zmLtfG6cE1iFUWyWjqbKFM6zWZcoA9aJqe7JK3TuHqSU5iCV5yB3LtitZpurdKrST+f5fLY1P/r8uSIkfnxNTu3AEKGbMl0rs1AfIBmweeafRUmWUh5JSVqhU5hOLvLSXtSMYzvLExZsdy1xmDjsDqK5axTGZEFSa4ceCzJW9NlVBcpitLRgvfciEnCkxMRIDB8iYz7cY88354+XeD/1838R6xFpP/ORIDCH8fzh4x8ecWynP07Sjn8rpc7E9CQQHgn6IpR0RI8a1qsVwU/sd/f40aGygAyPilAo6W4cRT/md/vJ73eqTKWV8KiUln1NWqdte5j36k9bn26lFAPKTzD2kI8k4TQTUT1p8uSZjMuwByZcitRmoDEDXzeJlYmszcTCttQucrUx+DQSomeKGa0cuqpOslVjSrh5Y/AtjGgGH9l7sFZiy2JRUpSWqlywXNZUpcO3ibpacn39hPtDj1ETm3XB+mLJOBYnny8Ap2UTiTky+R7vPYVypLllcJwbJGWYpoGcIs7MA/EcWC7rmeKgUIjavdWGGCZxpwgJW4g4snFOLiKnIBusARKUtiIvDGUh+neHoaVcWi6rC566Cls4dCHCwmqWhYpBeJXTNJBynMn3Z+dLa7LWTDGIVKZWlIVw1jKZYRwE5l45jKuxZY2dBtmUvOfu7h3ireY5HFpyzrgZyau1xppa9GCVtMRi8uI6rZg1E9PpokzI3MBoEdU+KkAcN5myEZTYNCpCiHMCIhzD8+w7A/vBM0XYjxFvDEllRt+SqwW6aAj1FUw946HnZtexayfuJs9FztSz7c+UEt0kTgo1cKkNjANhmrjZbrFDTzWOrBcLkg/oMBGnCescbbudg6Clf7cnvr0j3u9gjGhjSTO9I8QIwWBHaYMWZ9JbmcdgQ0kENPXCUTcOLiHGzDAMYoE0jBzGiRiEXylV19zyLRzOGmqnKWdSvARB0a88qexrKJ2lcIqiWs1J36xkdExqcyZm4TIex1FHObuUZz/BeQPKJ8Wi+TvMG9v7Qh3pLFgK6VuRjm2+ORoGn5n6xH4fCD6S0TiXiVajdE8ICe9ln3HOYwyM3QEIrDcVxji0Nuz2PVpb6qbmftuKkPXbW4rKUlSG9UY0ZgtdEaImBiAZbNEI4rvoBTkdo1BjlFTHIWd4ryHxQVV43uL8SCD8qf/+L7I+9vwPAuD7vz/96+Gz5/w4KJ6//vuf9wSKQVIOo8yso6MgcQqERj3IUj5/+hRnDGPfkXygV0qEwucES2mFSgnxaJ8Rz49snj767U8jnePA58EXUa7hYRC6z6euTwyCmXT7A9Fq4mELaeaCGSUADR/I/QQxoWLEBQlyny0KPrtwvLiwfL1OlCpRxIibB6laa6a8IFjNolkxTYlxCKRxIOiJISaCtlL5PF9hgVopnluZXVxu1rOGZiBMHTl5+v3E1I0QYKetELpDwBJQaSTF6dEBjiETxsyUAtMY8CEQvaK0JVVZcJQ5VkpjXQU5U1hNThqylowkzi7JxpJyYoojMYgYsSaLWG4RqOokDgHWCs8siSO0nk/62O1lwyFSrxZykSYlyjBGFHViDvgxEGevOGCGDj8+YyFlcRyI8jyTgWgYWoEw77uOzXpBtbiimq1rpr6bASsDbbs9QZencZwRYoqmaSjLikUjVffmYs3z51eEFOi7jt1OaCIvX84C1EajrT1ZvWx3Ww77HXd39+QY0VpR2UZMk+uacZJzMIw9MUbe31X7lITXWBqU04QML/cB1yxYPH3B9c9/hdreMLR71PgWO45cG8tnl5esnz/jvqiJ9/eknGjLkmq14vrJE/SrV4TQYZInz7qE7f0OM464viBeBExh6YcDShtpOb+9J9+2sLsHHMo5zGDwYSKRWVxeoZoSW1WSfMwBJUeRp3q0VEJ2WlFrQSVcAStTUi8L1vNISmXZcLRSQhi3VlSOjABhrDGzoPRDGwuEmj2LO5FTIOYkdkozGvABg/HeLAY4d7BXCGBfZL7mx2Sh/OgZKPH4BT5S1atjLQGkIAo2Vcng7xj7kRgydVPiCpnRex+YRg9YtAFjCtLQi3pTlzBaQDJtK7y0qs4sFiuqSgTwx7EVKsgh0jQVq9WSoZN5ryuKmYhtSNmijMMVDq2zqNE4jbMFWhmU2j1CKn98o/6vrtL71PXHJpTvK928v3F8bEZ4TFiVUhilyPP1b+aKUaHEcOARQAicK7icPQefPnnOMPR0fSfJV/K0fU/fd+z3O6Y4zd0BfZZInAX2I4jpFPQeOJXnX0X++0OFnz+2PpkiwdBCtyXtb0W3kywAghil+utHAcsQWZiJtYWnC8tna81nF46rymMSKJ/nVqEmKY3OFqsc9fIaM3ii6mmniaAgGU00GqzF1JV89Qx12Yiwa2FJQYjBPgT8lGSAPUPY4+Txo8ywtJqP0HtAixglu44hiX5wEN9ArEYpQ0xi5aNylhOkZFM4aiuKYPEs+2YkXQx+IsdATlE8x+aNwLl5qJsiKUh711oDafZWC0F0QVUSaOfcXko5IeTF49A6n7LwY3b2/gox4YPIc2klgrTghQcZxTg0zoTlaRzJIQqx3wttYZrGhyA499f1bGbpnKFw9lSJlJUlK0tdmXmwnajrGm2k+lPmmN3Nyiuz1RAwo7vmzVOJs4TJDxv4I85PBp0yNkvFYcgEBY1SNFqz0BqnDFnNwJXMyadutVhweXGBrhZMOeHbPaqqqJdLNpsNw82NJPtJ+HxME/v9HvyE8SXrpoSZz0gQcJAPAzmNZBXJSoxXDZ5kAaMxqwpdF6fvcMy80xkC77iOs418FnJkliv2ZJaHVpS4WyickS6G0YISPv782HY9b72q2TtSQCzxAXk3z/XOd06lHm0/p1+e/3N8n+P3IsVjY+ojm8fjIKhO3/dYQSJm1YjZcMoiA6djxE959gAMs7ZqIsZJOgUzN/T4XbyfSFnhikhKBhAajw6SLE2DdHFipbCmICEo5L4XOzLvM1o7qqoWwriSlFRMXD9yk31sKfj4HXk8tg/H87/s+mPyZx97h0+eaObjzO7hGtVZ3EKUnqEo6sFTUwAzHE/o6TOZuV3fNAtygrpuqOuGkAM+BYrDfi4IEuTuw8SQfBajH9DQx+CnlH50nR87LVppVPgXHQQB3e/Qewf3r09BUFmLzhJw0tBhdWJRwVfXJc8uS/7eLxs+u1rwZFUS7u8IUeGjIWgLsws9aKwtePrVVxzakXS75dXdW8bgsZXBFlbmfk2BjhkdwCVD6j37sYPcksLE7q4jeEgRSiVkfhUSw/7AFBO2adAR8mO6D95HxjHgp0j2Gp0dUJAwBMQVXQxQFUVVYYxmihlnFEZrpmki+kQMGUwgk8hpkhYBIjOkkiUlS3YFUWWGMMo8zxp0WYsYVZAWTAiBMYhjvdKgjKikxBixxs2ee2ZGZkmPXZ2MTI+XDnRDgDKgdCInLzObYyKumBFZmWnsefnDD5DhftfNRsiiFHJUjwjBC/KrrFhvVqzXazaLBcumpLKaMPW4wnBxuWAce1IKXF9fnba8KQzCkRsHOV4xUtQ1buZ0xRggBnIOc4WcZzcPjZmJ+se1DIG1n0Ehs6pBUxbUaaJut6g3P+KHjjEplLG4ImOrBdfPnvL5l1/ydH3Jtq64nQaKomJ1ecnPvvyK79++pdveM6Ysahbs+W4cWBQlu8WSX11uuFiuqJc1wY+EacCUGr+w+MkSIqAjzkbcqqFYNiy+eka5WoEWlOsxHMSUzqzB5BjJfM/MG/rDRqLngOSY3cO1zPyO4DE9B0dzalfNG+NxFjcnSdJViA+BmI8rnqj5fTIQj4ETwQQYNUtJKwmCx+B+JCwf21GPX/AhCObZlUFyeLFeskaTEeFvjEI7fQLn+CkwjWkGa0QWiwVGQ9sdUDpSOMNy3YiVVzfiQwDliDFz2N8DmouLK7RpKIKjPXREXzB0mqurz5mmkR9++JZ3b7fEGKnrJWVZcfVsybt3r4lJAH8xZyafPj2I/Mn1//1qUamzuezZOpHPeTi3xxGItLOt0GDO58RZ/BFVUsT40K49Sp2BplmsaBbI81QmEmm7A7vdjroSbuE4jkyzTN9RNu2sh8uc3cFpKnnU9FXY2brKWRFuGMae1zcvP+lYfHIQzH4g+wEVPYvSUlhNiEHg6FZTXBRUJWxWmi9fLLlclyzWGlcmsvL4MNL2Lfe7HWZRYwtHpQpyTOjJE/qeNI6oMOCIZBWxObEoa8rSYVIgTBE/io2M1L4jTS1D2KJomMJAFyassWQ1KxpUJSZEOYZa2kHna7F8wmbzDD96jo7XMSu0AW0yRdnMIJ2MLQqU1oQcBYWnNE4zAxYyIScyEmnF7cKglTiFW2NxhSgnGOslyzcKY0uiiiiVKI1UlXUSE96sM9ocldIFgq61FuCJVmQlQdAox9QfzjIlWCwamkVDjCOr5QpnDTFEDl1H1w+AUBWmEBh9wChNWRUoJW7N4k4wycxISXZVFI6isDSV49mzNVVZ4Zzjhx/fMfmJH19HtrsD/TDSdYOgR4sSa2WDr+tqJr8nXpQv5HfGcjjsGMeeYWjnSjJjrfAGie4sC8/QjUJeVEqskZTCMBGmif12y8u7G6YY6caeQyc3lEkDf/Xb3/Pj7T25qvHjwHg40IdA+fYd/XbHu++/Z7/dcu89fgqEvmcbIqWzvGuWrL94gWkalleXsj2khKpKrNIoZ9E+gdK4qqZarygWDaYsZAbp/WmzSfMA//2ZxTweA86DSz6OPoBZik5lDHkGsRw7C9KOPFYh6dQlOKs84ix/Nv9CIcCqY1CUwCnvc7yOtBKj4XQsFbO0046B97zi0afg+96+QSQT58dKJ8VosFoCtzFZMj2lWK4bBmsY9pJI5ZQZx4CdlUCcs2iVmKaANjI77ruBFDJaW8ahJUaNsdM8i4y8e/dGqCtaOJwhRG5v73CFE9m+bMnJChpcKepFzeZixTiJM0XXdhSlJsQPvthpqbO/HxrH548/jzR/w1D6Uy/zkw/66Sc+wJLmn6r3XnJufRoj6NCjXu75igSOSu3nbhmiCIV0S/TRkkkc4PPZ9QNK2t9KRmDWFtT1kuurJ0zTRN/3s9VYEkWg2R7sAWCHSG2iUWd+n8JbthRFQVVV7PZb/vlf/5OfODaP1yfPBKfDViSh2h05l+A0YRqxhUZXhqoUYevaWpz2qKzY7yOMlt5oune3HPYt7+7uccsltixZLCI5RAwKY14LZ25/YHu3w08DxggfKJSBlGAaAmPvIRXSxokjq7VAvP0E94eWQz9gjaWqAr1X7NpebGYyVN7TD4+hs32faNtEmBDkGAqfMqhI1klkspIEOjMm0BmfAkbPbSmMCDokmKIYfBy1M+eONUZnUbrvZ9/nHDEmSZvQjHP2HylmBfiUM54ZRWVn6bYkTsraiANA1pIlJRJaRQ77x8CYHAbS1BF8j64V1hZAIPlBAqZSWEZ2LpCKQhCy2ggFA+gOByZ/5PKNGGtwRtHtC0qTGC9Kkh9QSvP65Q8cuo526ET2LQS8jzRNQ1PXYMBoI7y1vsWPI9YVspmTyMETxpGh6xinUW4oo4Wk/p4vWNdPuCAcJubqJ6OIqiOieI1lVIoJ8NPR3QL6ly8pb29EFHymKQz9gNGa/e0t+/2eYRyZjCWQ8TkSRrmW9lXH52/eooqCctEQ/ICfBoa2FXR0FEcHEX1X+NFRaEW8u0fP1S5HoElKJDLv3r57lEkf9iNazRRpKd1mriQc5wASBGdaBNJCFibD45T+eHmf2swg9lDzdZkVD3/mwJZOryNV+JG4nub2ZCKjZ19Fw0NlcGybiQ1O5tCezdwz+EGCvcqcgFDZJLIxJK3JZoY1pFlVVInjeAqZFLKouJRib5aikNfHwc+enwqtRxSivjQMHuMVWg/EIGLgfT9QFAXOOqq6YhxG2q6nqRtQyAjFi2G49wLa0trIjDAphuHoRPEQ4HNObO9u5g7REaNxTAr03EA+j5JHZZ//ghXg8aU+8vR8fsLzB795/MR8lJV++N1ue/voGf0gbUmlH4KgmTtPRycVM4P/jHmY9c+tL+lSaD0HyOPMTp2OFUqC4InvnjOFc7BYUBQF2piTnZOfppOLRDrz3zTGylz+OBrQgj2wxopYfFEKPej9Q/ITS/0Jv6nTL7V1DxH/2GI5Hl519rfiNCB/dOLn8jmdwW/V2fBBWjAzMXZ2Gjg9/+zbfNBtObsw8tzLPvXl55r//BrKwDA8bKzVXP182EX/F9X8UB/816N3ejg8H3n3zMfP5HkKJ6+Wc2box9MjjJu/1/F4n81vHoiyckF+bIbxPplbzSf4OMM7zxCPBNqHHn5+mCI9emkl8818/hXU2Wc6U8E4PiBn4XPNyx2vnbNj93C8eDSBerwPHFU6zoPF/CnP5hiP95GHdqKxVq7r0/d+r/V3dpEdZcTU+x/ybKUs+pan71WY4wj44+dcnf/1X1U77WO77WMc5E/Ou04VpYwYjkuf5sHnr3x2T5yfw5RPe8DxNU/vOh/XfKooeHwbIMnjw30/f/bMw3mYj2uek4HjW5z0NOGBSpTO6B3H550diLJq/kujPP9/YaWUmGbzADgiu987Sx+5jj/6zU/X6N/8uDwAduT/5h/ykbvypz+Lenj3jFS1p9fP+Sc/1CcHwX+5/uX6l+tfrn+5/uX6r+P6Y0Hwk2eC1tqPZj4nrskp9z+P6u+X5PmDjPnICzmiHR8yvT8e/f9G67wKyIjTwelX6jxx/In1E784z3BPFcDjtscpm3y/aMhnT3xURT38+PzvU1v9VDlz9p1msMMZ4PAElNDnr//hG5w+m+IBUp/zR7/x0bD10XecK//TY9LD8x/aRA+Pf1CDf3iH4/c47xqk9yrR49IfMTj9r+PKWebIx2WOSjLnj+EhJz87NA/Vk/rpnFtMcT9EoJ6/1vH14AFZ91PriIA+Vcdnf06dl/ka8+GhErQzN/ZPrfcf8S8y+z6CLD5VQeTj6wGRDY+v+dP6ye+ZP/6/H6mcPrjzHm+Z71VGx/3mj37w8w/4wef54F7UH16Djzs3H3yYjzVeP2zRfawP9qc+9/uNibNuzUOt+tEjAvABAvun1icFQa01//P/xf+Kn/38VygloAWlYBpbbu9fc3v7ihAnGXI2K3bdPW1/4Ob27ekms9bS9z3b+ztKZ1ktFvzlL/+Sv/jV3+Lp9VP+2T//J7TtnrZtubt/J6VszgLtz5wGn9bY0/EQKyIv3mR6Vh8ArK1mPp6jKCoKV7LeXLJZXzGNnn/3f/Y/pWtblIL/5t//JXVTEOLEOEaCz6gwg32sQhmP956+HxjHACiWqyUUAWwgFi3VyrG8rvns6xdsLlZ88eISpokwjvzwh5fcvul597rHpoboYTxM3NwFpmC4fvaElAZ86Pj2dwdSAGssV08j9QI26wKFJkX49pt7AQb5zL/xrz7hyWXB0wvLdttzczvy7/1ft/ggAfB/8j/6H/DLX3zNX/ztv2Ds9gzdnh+//Q0ojTaWJ59dsusD37w60N+2LGrH3/+3/4I3r2+4vdvSj4N4y3nP8mLFoY/841+/pb9pYYp89YtrVG1RlePv/ewpjXMoDP/ZP/wtr97ecdNHnv9yyfOvFnxZrqlMSWVqfvftjrfvWn7z6++pKrHoscrx2fOn/OLrLxmD5+b2jv/0P/9HLOqSlBP/6T/8J7NeqeZ/+O/8W6yvFx+5gdR7f7/32/O2/B+92h81VD/clPhwg1bnO8IHrZzHT5Q2v2J/0/F//N/8ByKmYAz/4//+v82z6w3rzYocxRl+miJV5agrx+fXa0KMtOPIarWkrEqWq4aqLMWOzFls4bBFwf3dlt/81Tf8H/53/z5NqXBWgTai2N/U/OyXzwHo+4muHXHW8KtffMGiEZHsruuJWYx6F1VD30389tc/8Ou/+j03N/c8f37JZ18944uff8bXf/6XLDdrNlcXtHe3/P4P3/Hv/Lv/a3wIVE3D//J/+7/n+unzE6eVLAFaH+dNKVGaTOMUm0rmOKP3dD4ypQzYeUSSSUrN/MZwUkEqygoZYOqTg1NWs4pSjrS+5z/+B/8hv/7dX3Fz+0p4v1pxnH+qLGo3p+Ri3m9sljlXImJjZtge+Mf/9/+QnDPWGP47/+q/RukseX6C0lqAUHKxUZU1IPNS33egMtYphkmEFKaQKUpNVRkur5ZoIPpA3833XYoEn05CATJmEo+94CN+SrO5tmOzXp9Aa1mLFZafokgjZtkbm7qhrmsAhmHk/v6AM5Z9O/D/+o/+KTmDLQx//7/3K8qyQuWSMIooxzi2FLUA+8pmBg1aRT8k/JToh4k4jZATy3LBcrViuViCsYxT4ND2lK6EBId9S1nVFFVFtVySgBAzTSVylppEToEUArvdFuMstnToODK2A9vXW9Fudob1k4YUAsF79vcdCkNhC148f8Zh3/F/+vf+bx+Mzz62Plk79PlnX/LV17+Uk2k0SmWGaU+xtJgSYvQCOV9f8e7+Fft2S8Cf6sOcM8opxjiyqArWyzWXTy559uIJz58+4/XNt7gqY4pM1itB9Wk7a1zmWaXEYqyZs9IjKjPOVYegHXNW2EJUJJwtqKoFZVmyXl9yefGEoR/nGaB8r7opWa0rUnZ0nWcaA2mI1KWlqSxFWeLDxOGgOOwHYsq4SuGWBrvQmItEvXasnlQ8+8WSi4slz58tGXYHhp1nuTVkHM7B0l0w9Ymb13umpBknzWpTiGXPIND3iAyXq0azWCpWqxJQxJApK4GmJwOXa8XTK8MXTwsqE8iz3h/IDPDrr17wi69e8Pxiib1q8OMaursZhm5ZrhekPGKVp64zm03Dl1+9YBgmDoeeuhancB8mluuGug48eTKh6hodMxfP1rhFQbkq+dt/8SUWzf39wJOLNTGK3+OLny159vWCemeoVMmiaPjlry65fjaSY0apgFGZZdXw5YsX/NnPvyQ4x8vXb/nDt99itGKcxofwpuDi2ZLL58vH1fbZdfqQHT/+/acky/kUAPPpOR8I+X707vjwtx+7+R5XtA/KGkrB1XrBs8s1V1cbGfwDk5+DYOn47GrF5D27rqeqSsqq5OJqzaKR69s4hysLbFFw09S0twcWRUFtRVC8XjRUTUO9qLlaLdBGExaJcBEpnOPL51cQPTEEphjF8b0saKqSPCXiMKFDplKa69WC55drvnhywdNNw2JZs1xULNWadrs5FQFaG372iz/n+edfnmT9MpmYRV3HaIPNkcYqVoXiYg6Cw+TpfGJKmazsCX0dNbP10iQcV60o6xqSiFaorJBImFA6E1NkP7V88/Z7Xm7f0qWWREIZdWxroHM+2QedB0GXDWLcmnARurOZolKK9XJJXRZkZuFnELBPkmunqYTak3PC5wwqU1SashQj6nFKuFJRVobNcoFSmTB5jEYQ2VqLCcCUGEehVFmr6J0W4f0xinB+YXFWU9eOxaJBmUCMkbH3TDGQMrjCCkitqdEaxkGcQR5LjwnieLVpqKsGlUriaAk+0A+JalHgKoMtI8qIjZQtNT5kbG9J3qBypjYlm03Dar0CYxmmgHKGulhAkgS9qOcguF4SkyClF00zI+4hx0CMAVOCK0vKpiJ2O4bCkntPsg5dWDZXDWGa8ONI8AmNoXQlm83y7M7801Hwk9uhKSTCJFJWyShQicFPTCESUgZl0LqgtDUqa1LIBH+ERsOhPdAPHf3QUzqLD4G7+ztevf0RHzte33wzi9e2BA9aO1yxIIUOH/3sLC3tl2kS3cmcOPHJzjceM00YY3FWnC3Ev21imgbGfuDcdThlBdpSupJp6gg+MU4H6hmSfXW5xAdBg04+0Y+e/dRxWRfUTwqu/6yhXGmqC8viucNUipaR1zc33L++Z397oCkcX3694qunz2l3nn/qJ7oBuiFTuIkUAoQszgBzRqq1EyiwqmaEVEAbg3WgTMaqiUJpmrJkZx63Z5SCL794wsXK8fK3f8Uvfvkz6qpEeSebr9F8+4d3vHzd8ptf37G8LNGlo42KP3x/x6//6Y/86pef46ygwMJhjwqZp+uCz39+TVM5vt0ObNYVTy4X/OUvX7Dbdvz6N68olOXJZsWTzx2bL0sW15Zf/8OX2Gi5WKz5t/67/23quubpdcH927cMbctXT5/x4vIJP3t2Sfn513zz8jX/4B//I969e8eh7fhTYejxpf5hyHsIoj9NLv7Ya/7Uz//FtOoev0pMItyQYuBis6QoCkASTmsMWslmHX3g9W6PcZYhRi4vMosmUlqLHTzGTrT7kb6dRGllGHFWsb66xDhHUoab2z1VWbBcNlxeX1A4yxQ8v//tN7z8/hV3N3ua5YLLZ9cYZ7m73fP//H/8I5JPlHNmbgzkMPHDb38NyoArWa1XvLvdPkoArDJYZUgaIDIzUFE6orTYrbrZfNvMB9cqQ+0MRVZEFEElPHIPhiQx72gua1UGLffOKQDOQsxagzWiMZJTJCfh8SoQtGwW4YU8C7gfz6/KCkfEapFNK4xBF5b5leRxzqILB8qIL2YIdIeeaZyk+hvHGWWqCGMQdR3lUMZgjcFrUazxU6DtBFHrp5HJy38v6gatM0YL8thaTVkJNcAXAWUnlBKJu9dv33IVLynrGqtn/m9I+JjJCirnUEaRVMQVhtpajFvSHgbU4fxKVxSqwOEAhzUOmzXJODnvVjH6jjgGUg5Yt0ChKZxBu0a0QYdAjkkkNNUsrZcMmkIQ6aYQ/GyK+KnDx4lx6jC6mxWQCtKMDi1KTbOsWazW3I8HQaZajZ7/KCVya37yYr7LDPSZJvz0GFX+x9YnB0HjDLacswctXKfd7p5td8euu6WyJXVZslzWuFs5WRpzCoInVZYsJ3X0nm7o+P7Hb7m5e8nN7Wu6vqPvWrRqKJy07UIMJ8sdfXQxzsce/yw/FY9Z9pxZx4AxjpQiZVWijYhVty30Xf8oCPrg8dOEyo4cZtJ27KmKiouLms1mwTR5oheH+EDCXlquv1iyflahLyJBe6ZxYv92oM0T77o7Xv3uLbs3B3IHVelpFyOfXbygdJYnT9b04y3VMPHkmebV956bQ3siVEciISqmSbHbjYzjxDR6YoSicKxqyy+eb/jiacGLC0eT15S5Qqs7jlWMP3TssuKbb2749sd7XKF5ceWo6hrjHL/9Z2/Z3fbk5Ll6dsWzzzdcXiy4WC7YNDWKAqUrVFHRLDIbq/j8c0uBJ0bPN9uem3d77t5seVo7NJm6hKYQJYfeafo2ME6ety9bXDLkldAS6oXl2WclJi0YnOXnP/sZ68WaYrnCEFgUmj/75Ve0u4430/1Ho85DMHuYL/zE5OHR+qn51N9kXvToFT46EPmJ5/1EEM45c/tuD2Pk5s0dP/vas1o2GGuoSktVOC6uFzinKAyM+wMozbJqGIoSYzRV4VD5gW5iDXz2xSWruVL76usvZ9eGxO3rdwzDxOQTAS02XOPA3e2e3X5k2w7c7ge+fXXLomlIMfPkasOT6w2bzYIvvrymKi3t/sDlbBP16uVrfsyaN/f70/2lAEfGzaEnaZFvS1kst7QGM3cnhaMmDu4xZqHUaE0EjFEYDCGLSHPCzDZ+CouI32ctvp9KzcR+rUhZk7Pjqqh4UjYM2pIRCyVNQgMFnPiuZVXhrKicVGUlFlO1w2bL6x/e8O+r/8vpnIUYiUm0fENKjNPE3c2tqNbExND3p70vBYQbV2RM4TDWYGyBdRqHYbvtiDEwjB1KRRHtTooYZ/UqFMYICbwoCmKI2H7EmmPnZwsK2q7DFZIQl1VBoWQ3SGmaXTAmlHIiNOEUZe2oavf4YkwKgxQQ4AhoJq1FnchqgrciYGI0IWlihKREZ1UlqdZBiqCu60S1yoNbGIzSDDoDEzFOJK+JaSKnkRQyPmrGbhZUmGUqJz+QOzWrASVR2HIWWxSsVktySmKBBbMtkxUdYu/51HT1k4NgZCIyiUacSoQ8MfgD/binGw8YEikvT1JPGmlPSDYEJIGFhxiIMwHSR892f8ehzRzaA+PUM/oBqx3GRLlp58dNYcLOagVHMEeeocxHkd4TPDpKi0I4RxM6QE5iveLDdJapzq2vLIotKkVMTlgLZWlo6oKqtGiVqUpH0plkYPGs4uJ6wWJdcsgtfgy0QyQdelTITNvA/Y89h5sJGxyxjKQx0bcjRonWY1lmMPDkScH9OyXVYD5e1EKalwtoYhhGCYJJNtLCGdZ1wUVdsKk0ZqkZV/phFp2h7wZM0tzed/R+pKg0P/v8S6rCYrSFAAbFojRcbCo260r4P0aqP+8VmAKlVmAMttBsGkccDwypAxzj6JnagXd3e2pncCpjtWxCIUTyoNET5Citaz96usOBqtFoJ47RRVGwWa9ZLJaUVYNxhroUP0ajzdx9OLuY8+MAqNRZ8vPBvx+vP9UW/VNV3jm450+t94OtAAx+4nkZwhTp2pF2H7i6Wss1UhUUVkYPxhpskPOjs4wBwjgyjRO2KshZ5og6J0iJsrA8e3HJxfWa5XrBs8+fzteUp73fMQ6yMY5TECWnXUs/eAlAhSOMct0ZbXDGcnG54sVnV1xdrdhcNIhHSaJwWvR3p5F+EOPl869pVMYqCHOHIwE6i4mtPhZvWQ5NzFmI/7Pgu56Thqxm4FBW5CT6SEmQaJj52Ob59Y4aqVYj/DRjuWyWPN9cMO7XkIME3ySk/0IplsslVVXRNA2FE+3Qqq5whaOuC1TS6PCA9cjANEkQMlbjJzFl7rvu5Lcp/orH0aO0VvUUcZXDFpaiiKTsyNmSJ/GZHKcR58TtYhim2epMzWR/EY8wszi6T1lMqhWUwyDt7RhklmksxSyennNinFVYFJHgFEbPVB915NudXbf5QZpPzYLzwk0VPp5RdnYosaRRZB718cRmsVUiK1LMdG1PTIqspL0v0pWJlAKohIqKlGcZrySt22GcMFpkFbUxeG9ISpGSqJQZo2E+DtbZ2b/04Z43xszf+9OT2k8my7/e/prq3lNV4uoQU2A3vua+/ZGb+x9Q6yfUtWOa9pDCSdKpKsS/bru7J3WJQ9vSVIvZWUCx3W2Zpg4/9eTZt2wKHqVFjX8KntFP9GNHkQsKVUhfHlEkiMRHChyiap6JQWxw9lphjQjsPn/+OVVVnW1QikVpqa1iajtsnHA28/Sza66vV2xWBc5KcF0vG2zQ4ODq6YqYA/t3O/75775jfxg57CcuS02hDC5U+M5gxyUqWrxJ+H3kn/yD71FAv49QeFaXJf/mv/k1vk1895s7tPIonXElpOwZhkC765lGsXJxZYlKkV3upcWSHJe1ZYEmdA8K/pnMH17dsaxH7tsBXUnWp1cNyWfy0POrn1/w1dDghzUXX11SLkt++OY1d7cd+y5xO+xZrEsucoHPK4zR/Hg3EYcFKRZUtoB6h1FbdLFAEbG+Y+o9+8PA68PIkxdXXD3Z8K/83YLYjcR9y2//2T/DfFPgLiC3mSJaCqNYrRounj3FVQWUhtViRfKRft892lQ/bI2eA2L+eCn2p+eCD8i4U9vrT0Hv1E/+zwcP+6nbUinFxXpBqQ139wfGUUANq0JzdbXk+mpJWRSgLYuk+OKzxDROdH3HYV/iESCGUaKtulg4vvzqCbr+11BaZmdVWWNnsW1LZrdruXm3xboCPwXevN4SI2wu1/ztn/8CcsaPAasd5MzkJy4uVjRNRbU4VkyaTe1YxoZiUbE7eMyr20fHrHGJyibGoIizY7plrgCFX02MmX4KuBnxnED0D7OAW1IW/mBhnAS9CCqLOIRVVgRMkKTOGUVptPjWoah1wb/+l3+bXz5/xo8/fEFKE+QwuyDIBr9araiqinJWhNJGo5yalU+EMB+HdDq/OSVe/fgjdVlgnSFE6SZ1hwNxRuQapU+AHpBgaUuN0kL8vr3bYbTD2BJtC5RRKAdlUWCMom17UlTkpFmvVuQkYuDaqtlbJUlr1Rmunl7O0nKe5cVClLK0xY89IXimcYCoiUkTR82UMt0wkfOaw/49BDFin+bThEqJnCPWqpNvYOkW8lnRpNhBEvWXvh9IPtLYijhqhhh59cM7jHVsLi8JfiApGIc9WUcwmdKVKKT9rSJzG3lCl4ICOrR7jJswU8XSakzpSEVCFQ5tNeMoxsfHBPN4f1VlRVX6P3HXPaxPCoKZzDev/zlTfUdZFRglbc6u7dm3d2LnEyN+Eu+o4EdyFrmk4xe2hfTDRX5IZnhD39H3B6axk8G5koG2IhBTICZPmjUOYhT5sBhFMs3M/nwpKakI8xm8P0e5ABXosSMYizGWttuRz3QZQTJGpwGtcKXFWcX1kxXNssQVelalsJiVI7WKKXpuX+7xacDHkXQbsaOm7ktKDIWxOGogEZQ4oKekiJPm/qYnxcjhPtFcGlyZGIcDmkBdWhaNJumIqTwxB0hKZq3GSKacFOOUUDHxw9ueuoBnTx0qzFn0+cWcwSnDuip58mLB5ZOGp08uuPvhlru3W4zWVIXB6ZLvv7+ji7ccgubH7++4vR2wpQU1UJUtRpdoK23o5KNU5ymRgsOoNd43FC6zWBmWy0zKmjhFvmxKPrtosIWi28FtmOhNJOaJYXLUzlLXFZXzOO0hR4yWWYTTSHsvP75JP+Wi/ljg+lPB7EHY+Bha1cO7vX9sPwGT/lOZ6Kkl+ngUw8XlgqYscJXm+YtLLq9WXF4tWW8aqqbC2BrtEq6sKZ2j6zqGd3eivpETfd/j5nlTnkQovakK0ZbViuWioixLnLVM44AC2n0PSqGd4fnnTxnGgZgC9WohHR002csYw04ea52AT5JUpmVTSbYeJoYhzILt5xlLYrd9x/piTVFfzOA1RY5g0TONR5zBI/lMhFuCeUY8PGOOhJxQKczjlMC+PQhgzl5hXIU1onrkjKawBjufgzEGmrpCqQus+YoURlKaMLPsllKGsiwlQTDz+EaB0omUIzF6urblsH9wKs85M00TioxLjhA8KSasK3DFUZJuFv9QmZCidCyM6CRrqzE+QRZ/TYMSya/KsVxUop0fEznKvpYI+JjQ3qOTJiMVouBtFHVZkUoZNVVVjZ7bpJVx8+MCcUaa9u2ED5l2iGgD43R2IWY53i5ZzFxMpBQIaYQRTEpoazFY2ROVvIbRllwasslUtqIua5wrudz02EKC4GqxhJwY6o6QJiIBTTG3AJLMejMoDNYWWGcZQk8KgZhH8TyNkRgD2StU1oR9z9D1s95oxCipvtNH942fXp/sIvHD29/QFa9FlkY5jLJYHF034uM4Q7on9vst3g+kHEgExHUwop1FGUOK+bQRDEPH0Hf4qZMLUoPSGa0kW4opnHiIKYl+Zpp15IRWlcX4NR9bJTNPDfGrSkG8qkw0WOvouh0xPD5AloRTGW2gcY6qsjy53qAdIvdlkBvFlfRTxvvM7astUzgQwgCDwgWDCZaqsDgc1tQkNYEOTGmaqRya3XYkjIHtTSTbmmaR6ds9OU1UlWGxcEQVoI54n0hZYWflF61hnGbD1inyw9uBqlT8vFtQK/BHN4DjJoLCacOqKvnq2QVPn6+4XC+5+cM7bt7tubxeSNZrLd/99Tte3XW83o1MUyJGxbIpcW5k7HuKYkRFh89ZBK5jIEwBpQxGNwxDQak1blGyXAyomDB54Mu64KtlSVlGtkTYOd6ZxACEWFIualbLBXWZcUayTq0MRhlxStdq5s/xp2PfJ0FA/zi28/H/PwAl+BNt0A9an/lBFemjz1GPq1alFMt1xcWipq4Nz55fcHG5EqRe6TCFRf9/iPuPL0uyPM8P+1xp6gl3D3ePjBSVWaqrq9XMAOAAFBsuebDh/8g9llyAwCE4nJnGgESjp7unVFZVisiQrp4yeRUX156LzKru7HOAQzsnIiNdvGfP7Nr9qa9QRZ7jFCm7eBuF3O4zb5LIMAxEa0GI7ICQIoU1hJi5ok1dUM0zpaFvcJOjKDTeC7TUrF9csDvs6KceXeXujbUFvhsJLqBHh5nPWqa88ZmiyDZiwdG189o8cprmz77ZvOP02Sllvbx3cpEholRCH8cXzD6G3gExz/NighhxfsrfSx4/He6f3e32jhAjZa0opZj5iEdgS/4rzc4SUgmqqmRRXRLDSPAjQigEuSX4hKPKkQ/oss+hD+z2e3a7HenR+pmcYx5lEnwWJjfG5kRBqOyxmhJJRpKPJMm9ILzWCmP8LBSdua9aa6qypK5qlITopjmACcbJz0htkFECihSLuQWZKAqLUHn/tFaDiMQ4UViDQBNcYEiO4B19NzC6RD8KtBU496D8lEi44HBBz55bWUM4JA9eE5PEyhKBRUuLNfmaaW3RBFKAUlrqqqEoSs5d1lten6yprCUGz9gNDK7HB4cQar6fgejcrPijUMqgtSbFjhizbVxQhhTyGsnMAAh+wA0uYzZCIMoZKxLjvX7u9zm+dyV4dfuWTm+QRlOKAisNJ6sVKUqkVgyugzbhw4SP2dooqoHDNBInMTuCB5Zliet6ehcomiJz8iioqipnfN7RDY7gewpzYOxHpsFnSbAoUMIAWdk9yESImUPnfe6tZPJuvrg+OIQXBJWzpM32mrEfHwXBRJwmpJOUGi7WBXVtERGmbElIUWVx35QiIdmchbkexozorIVGaElSEj07xycBSWVL2Cgjccozx+A1wcvcvp00fRd5+eU17WGgLOD0tEKVivrslEPbk6JiVb7AecE4Bn73u98y9onJwW9fe3o/cHK25cW6YhqfFixaekSa6PuREATTGPnd33/J65c33G4Hvrjecxg9d93ItpP4qCjMmqrI84eirLFljYuS3ntkyvyu7rBjGlrcNOB9rszv7jSrxvDxZYUZPCoqTooacRhoX72jC46+D0ifWCjJqlpw+eO/4pMf/Zjnzy+5KANSGYQqEBMwOUQY+fnPf0TR1Lz5b/8fTySQ/un1+n1an///O759bikmrt/csnzxjJ98dMKHZyVROP7mP/wdq/WC5apmtVzihpFu1xJSwsdE6xynTYM1hsJqhEzE4DBaorRmUZTZyFlKJuc47PdE7+n2O9zQIkSgrCxKZX5V6Qt8Clxf3WBtQV0vKLXCGMWqqWiModAKrQQuesbdgeu943Doub7do21JcA+fzgfPv/m3/y2vr77mZ3/+V9R1FqPf3e1YLlbZm7K0TN7RDT3X11c4NyGSZ+j2eDeipKaqG6q65vXVK5xLkCzbbQ8oPrnuODm9YLE4ZXu9JQYPIaC1woeJu91bSAPWCP71v/oXVFZnZ5dsg04UeSaW5r/SPJcPIXHoO16/+4b/+Itf8Otf/fYJWT7Go6ZlFnRWSlFVBVIaBJIh9SiZkBpKXaK0pCzL3GoFiqK+F3+WSqAUyOgzYlVqmkWTvTUnTxIRZA4OYj7HGDPgJSWB1gal8oC1P+TKv6wyrxoEkxf4oIgRgh8hZTHxopI8UriDlOj6A0IkkpbUZYMpNaf1CVW9pigamsUaawtsUSCN4igR2JQLZJLcvr+hLCvKqubPyhohNVKqPL8eBpr6nMmNTMHhYk6oYxiZhgPOj3TuAFHgpqPXqgBk1nUdA9MU8MKRZEKV2RYu+jxLzsEvt4VD+N8AGDONDj0o1JGWoMD5CZIkCcUYOmLyuDBmZ+0YcGHCh4iPib71DH1PmCZU0VAow7Jao+QSLQXLkxUhekY3stm1JOTcGrNYDVYXaJkJ2cdszbmACyFDy2MmrCLFzIV90Col5qaqT+FbwBhIMyCmKDTWCrRKHLoBJyReyAzHloqYBKPPn0XKjO4SYs5m82QepWcBaqUhZPHm+VtzlmpBRJCO0Xm6PtL3CSUNi6VkCBJbG05OSrSRkAznq8vsNBHh9u4dm7s9u6GjHWDXJm62gVURSfHp1upCJJCQRqGMBqHYXB+43fbcthPvDyODj7RTIkqDMplTeaScaFOgtM02QIlHETYnGQiVkawx0nZTbuuIyFIGSgFLLdm3GakYRaLvA5vtBOs1zaJhef4Ji+UlZXVG0ANDiEy9Z+EmpqHFDz0ni5Ln56unihgzkOIPR7ljI/MRWObJLz8qKb8Tif7gT/2ThxDfDyjz+Fy+azeUofxa55mWIhKmwN3bG6a2Z9hXTOsuk4Vvd0hjUYWhPllijcZYTVUV9+dtjUJpibEKbQ0xwe3tjpurW+6uNwxdyzA6Du3A5YtLyrKgqizDpJGDoN13hDplZLXQGCUzStVorFYZnOAyQnJyGdBQNTWL5ZKg1KPPGbndvOf1uwpVa5p6AUjabc+qWVJVNUVpcN7TTxNXN3eMk5tdxh2CSFGUFF5hB8H768zjddNIe+gz5FLc0HaSxcLz1e++ZOg7pqHHFJpIoBtuUGKiKhWfffIhz9YnnK2W91CKLKiU10xMCZFy8jpOI9vdjpevv+H121dc3Vx9a1XkPSiJiFDc7zmPATFivufGarRR2MLO45rs/SggV3Mi5so1JEJwSAVJKpKIGVWLJASBmx5RoYRDGYmyAhcGYhIomVH4UipSkHjnczs2zMEixBm0mOZOi0MK9+gTMVNJspm3NQ22KDBFQtsGZWoSBhcEYfDUuiCEjAIV0WKVwRQlSM0UARcRMiKFoNvnrt++6xndhPN+7hpkwwHnUrYjS3ruCiakGHOVPlOEIrNHqgggYzb1jSl7Uc97VJpbof/rt0MBPwZ8HxDJEA0EAS5MHO2H2rlPmwLsDy0hRuqmZpo80+Q5bEdcOzHte8rVBetqxYvzj2nqkrosefb8HBccw9Tz5t0VwzAyDAGZDEaNGWYsxD18OEXHOEwMzmWC6txGUzygnXLZnAfsIcVsAv8t48Y0BWSMVKWmMCBl4G7b4oUlqoIi5SwsEpnGOANUFFrXpGjBT/NFT5jSZhd1ZejDRArZlglmtBsVQkUwHYd+R5QwDucUZcnFpaJ1HdVCc36xpqo1SpZ8fP4pp8/O0IXh3dWXQGRz3dINgu0B3t94zpcRox+hvBL0LjAlsIsC2+SK4PrdjrdXB7657Xi79SShsEVJUy+xRUldr5iBt3kWaQzS5E00W6dItC7ynCJpIhMhjYyuY9g5ttueZ6uCZSUQDezuRmI/4Kzh0I7c3hw4/eyc58+e0Xz0M1RdE6LlkCS7fs/tZsvHoqU93OB2e86agnS5+MMSVd85joEvD4MffuURkjT/hycqgumPRdSnbVH4w0Espac/870OcZ8V3b/TYlFQVSYjlUfH1A3cfHPFoVRsSsNu1dDuB27fbynWDSfnJzz/8Jy6tNSlZb1akD0gw+yMLkBEbKGZnOf92w1/+ze/5j/9/eeEacqCDGXByekJZ2crVic1/dBz2At2d3tijKxOG7QWFFbRFIpCZ3SqUBozb/re75FKcvninOfPz1nc7Z5cp83+PeGbkTd3r6nKBVIY4qSoi4bSFBgjCUngE1xvJ0YXmTycnC5pmoolJfSCFBw37wv6LtEe9gxjByS2O8HZ2URTb/gP//6v2dzdst3cYBuLNgJdOKwOLJuCH/3gB6TPPuPZ+iQnSnNXWpDmwDWPFGJkfzjw5v07/u4X/8Dvv/iSt2+vniTPeYCVwTJSpEx2927eiMX9PiOVoLAlpjAURUEImcwuhSC4SAoebfJmHgVMo8mm2rrCpzw5i0kSncBPCa1B6YgpJnRVYStJ2+/QSmCVInhNkongNEPqiSkyTeOsPpOfYakiWgSk6JD0PDnSPJyLkqY+w5YVQQ0kWeGSYew8zvc457i81IzDwMuvX/Ls5IxF03Dx7IxhdIztwG7aImTuMty+e03fHmi7XebxuUBCYrSksArvegQBZSWlKtFGoKTL4ywtUSQciWEICO2ROs6WZhkoRTxCcQNpHsN93+P7+wl6hR/IZPMaYpHIcONc0luVB7l+ikzeE2Ni3Aw4FwkuoVLFxbNLnv/wnOfnn9BUCxZVzmSl0gyDJCRJCIbPPvspzgeur7f3w/6qzm7PwXuGocf5kXFqub65Zt8eeH/9frbgCNm5XvDI0TsvAJ3lCB72ImC1qlguK6SR3HY9MQW8hCgTSXmmYCGaea+UmEKzOKlmR3tHf9ghBNmKSGc3ax+5J9p6qUmzAkhwuS/uHAwu5VlFs6IqSpRQ3LSBKAKb7RV1s6QqK0ypWZ5UrNY1f/EvfkCzUAgRKDAUKnKz7/n67YASPOmDjz4iC8uHH10SfOD6asNv3u15fdtxdXAkZbPDu9YIZRBSE1L25VJKke0LJZMP6OSyNxgGbUqUKbFlQ4jzoBqPFAkjBcQBcAzGgkyE5Njcdez6iZvesX7xMeGjT3nlNN1hot73tG9fstvt2e4OjJcGvx8YnCTGjm6//WfAnR8FQh6BWx7pwIsoKbyhmAr6xUiQme+VN7T0rQj5h1/9j3/38fHPaMgKQbOsSFLw6t2WNDo0iZ//+AOUkSAEX7+5YRwdotCcPVtxcrqkNprT1YJmvSLGxPZuz93NDdpqirrk5PyEYQjsti3/w//w/+Xvf/0ln3/xDUul+eD8jJ//9DlCGpLQLFZrbq43pOBZNzXLZc1qUfD2/XusMtiPXuAMkDxv33xDYTVVaTk/O0EZgy0zR3jXP2yqKWWrMl1BsprVySlalfTecX3bMrS3DP1AiAKfFLI+p6xPOH/xGfWyRhnN+13HYduy22zorm8RTJSVI8QOIROTN8R0ilQFRdGg9B4XPCfNmrIpMI1m5gPxnz7/iskltFR8/OEHWGPwzjNOI0Pf8+WXv+dwaGm7jtvNhjfv3/CLX/2Sw76j7/on9zgjbVWmbCiZg4vM9ksiCbQpM9ldw2E3IqRDa3fvwRez7ghiRtAbA7YAbQRSJWQJylqqaGaVGM80Bpp1mfmiNif90wDtTmGtIZUWhEUoBcYSxUQMGSXpfWQcPWo+96o0ed81T/dDqfM8MMTI26t3JCHpw4GEJSaZX0NlesI4Bfqu4/PffI6Rkrqq+Ku/+HMWq1PKZoHvB9w4snVb3r5/SbvfstvfMU0T3gesre9b+c71CJEoSwNaUosKY2uECAgZILiMRfAeokPGyDiAmwTBzRa7UjyArcTDrPOfOr5/EMznweQ9VkekzANNrWRm8VtPDCkPcX3mhXnn8r+9pKpL6mrJ+uQZtqwRytK7kAOGSvg0EKLDxxFTVsQkkDNBtCpKLi4uCSHrxA3TiHdZaQChkeqW99dXmSs4b3jZ9iZvRFnUORIFs4bhsSUmqCpLUVlQia73OJ9leXKLIrc6hVCzHqlBa0lV5azMTRFvsqeWmRdPjBAmj1ISozVOhVzSSwlR5+uWxNzXFzifsCYhtUAbzRQy52i5OsUWFbYs0FqiFCwXltPTiovLJTIoZJo19rpI8oHHs2BlDaa0lLVle7tje3fgajewHwMuCZSZW7cyI+QQsz/X7BOWSPP1u/cvz9dC5j690jqrbZBBRVplvVU3tRBGolR4OiYv2R5Guingk4DFmrA44cZlnmExOcarlrEfGHrHvrekoJHlkmna4tz3aWt8G8/5qMQ6HmLO9kcQA6geitLijacn3Mc+Mb/E8bUeh7bjy31bIP37Ho+D6LebtGWZEYqt7xiGkUpLzi/W6LIgKcVmymayISTWpysWyzpLm4ncwrq92XN7dcP1uytsbSnqiilGRIDN3YGrt9fsNjuGYaIusmRbWRV4HxiGiXHM7u2JlFVStMZqgw+RFBx9PzE3VbjbHFg0JVopFo1B6Qwucc4zju7bnwylFlTVM7RZI1AM44bdbuCw3edZvzSgK9aLClssaZozpJQ4l9hsJrabns3tnng4YLRDWU+IHTLNPDMyglsbi5R6llnM4wltK2LKSjHXmwPLqxtOl0sWiwWFNfRDT9seOBz2/Obzz9nt9+wPB/Ztx+32ls1mh58Rso8PqUwOegCzu7k8PktH5KvMFWLwM0AwCPI2kfnNcl5L4mgMO389hrzRK6lBSWyZ/ShjimgrMWYWPA9ASugkEVGQZjJmShIfj3sfSKERzIj5ufyVM8L/iVy2yFqsxlpEEPRji4/Qhw7vB3wQDP2ELSx1XdP3PV3X07Yd0Tm6omW73VLUC5bGUJWQhoG+7xjGnrY/sNlv5paxyMm3SAiZAUzM6j+TC5iQkCLLLKQYIIa5xZlHXylEYpDzGGi2d5u5jXmk8/2T0O9Plh9B6IRzkUkHSIKpTRgjsVailwnvA4fDQHT54icXEVGjheWDs0+oyoYpKL56c4VzgWEYKauCwlpqXeF9bq0lmZFWq+UJ54szTpdgPyiQpoISTpW+bykt1xe8ff+a337xe2IYiTGgVSQ98rtLc5kcU2Ty45PPtTpZsFiX7PqO7RDpRs/ipETLTKvQakldnXJ2+gmLRYlQgUP3iv02E9jL2uRNwxiENDgfcP4wI/Fy/1+JLBI9DAmhIrYFYTRaR37/+zc0dUVZloRQkqLAjRFrT1g055yeXhKmidt313TbOyor+MlPXsx6ip6hW3L37pq7q91DEBSC848uWZ+v8cPAX//Nr/n6mxs2o8DYirJezr5pOXvN6hxHgXKVnT101n7U2lAUFQKB954Us/SVNhptDEpbqrrO6hqlQQlH9CO379+w8x23hw2v3rdYrTldrZDLS/r6Ob9q1/jbHWk/8VFXc1YWXH5wCmUNxrH+2ZLxy18z3o78c2Eu96Tme0VlEDN82r1OiL1D7A98UH2KXya+Kd4Rnswaj62V+1f8Pu/K49brH/uJP3y+gvOTJeuqzPppBJI1vPjsBYsPPqA8PeWnU8SPE9PQs3n7luQ9RWXY7be8u77iP/y7X7C923HYHjh5cY5QIkPHJ585pYeWj0rL6oNnLNcLzs5PWK4Ntzc3M/qxw3tPxMCs9mQwnJ+d4SbPu6sNZVkghOTQj9jSIrRGysQ4DlzfHggR3lzdPXK2l6yan/Lh85/ww5/9Kbe3e+5u73j51Zdsbq7p2x1NtWR1uubig0/49Mc/p25WmLLi9Zt33Nze8fnvv8ydn3HgdOkRZmJKWX5RSsP65MNsXKwLqnqBKUpiyiLcwhmSB4RFCrjdjYzDK968ec+hG1BC8OrVS+7urtls7/jlL/8TwzAwThNolYOYiYgkeJQ6gxAoW6OkgQBReKIQVEWBkhYpFOM4zht4Vsc53v/gHEeIl5oLiMFFApIkBdE7kAHZdRR1Q1FVFHWBLjW6zslwCJFpP1EpRaEUz1aWKYwMbseY1gRncLEAplnoY0mcDH4cjl62TFMiEBjGB8CZlJKzi+dYU+D6xM1tl6kZWmUZTB+YpomyysICaiatl1VJHxzOjby/esezi0tO1ysuXpyw2e348usvUVrhYuB2c8t6fcJisaBplllwQILQuYvnE7S9J+JYLGqmqWMYttQmc9ON0fTjjLeImaphjMwtZmXQ2mBsgTHTt57JP358b4pEmhmpUihSAO9y9UGUCFSmLsScoYS5rWRNDUEhks7luPNEoYhSgFHIZLBVRVGURBfvSaBt26H0iFYWXy05SqRlekRWHslzK4kfHdHn3nyKKbsikxUGQvDzZj9fjhSYRv9kfxudYwyGqDRBWryQ+FhhZE1ZNHz4wY9ZLZ7x7PQjNvv3dP0d+92eGEJGSSmZofxKzhwXnSkdSeJDyueAQEuB0ikjab3BxxqpE8gSRAM0NM2aRMLHiThZ9puJrw7foGIHoaXfHfA+koLEVBqhCopVidQSbIX4jzsIWTfx4nJNWVu++s1L3t32bLqIbZZZnFnMKhQiZ0xibmmL+UHQJmupyqOjNLmFo7TGTZlL5r3Pc0OVZo6VQUqTlSq0RhmLNCXCVtjFAi0ESSi6QeFazWAN+xuYbjx2mqDcYPtbBlOidIlt1jTPXrDqH0l1PF6Q3wopf3BsmI7gmISIkuQS4a7DjxCSYm3OSVozJskd14z0D3PD48Pzj7RHv/u+x1nYw7//0EN43519FDPL5ToLYi+X7G5ucc7x6u0tSw/1ocdIjVZ5hlI35Tz7k+xut9zc7nj//gY3BZJSGSmXEmHKrVA/Os5XlrPGEEJiO3hMSrjJ48KAkB3Be2IUxBA5bDuMMQy9Y1HlxNMZN6uhBD7+wXOMBO9Hrt6NTC5w6Aaub/Z88/72vjoWQvDs7GO0bHj5+7fstnvGvmdZLSguEvFszcnqlOXJGafPXrCsFUZPqBio1cRkI2cLTaxrkqhoKlAqINREU0eUtKyWH2FMQ0ywXK84n84Zho8plxZdGopqmZM9BJUMOWj7xBcvryF6rq9uORz2tF3PFDUuaXwKWeCdhEoJdR86Hu6y0BYhdJ6hiaxq40OuWgTpvisllaAqNCkGxmnMaikJbFlB1v0mpJQR6UFmtZQkiW7Ei4BIHr3IfMyyKgijI3mHCJKzE8v5yvDDjxRCGmIq+PylY9tGbtqIS5GYYBI9Q+eYxoCfsiCHtiCUyAGfh/vV1CdYYxhxaDPkPVkqYnQ5CLopJ0sxZvK+kJyfn3P64x9SF5bSGE7WKwprWK7ySKcsKoKfqKsF7aFjtcwKUUpZYnSEMNxXw0IolLIoVVCVS5QMCEbCuCeEGZSoDAhNWdQEAX6OD3nfT3OA/Ecf2yfH92+HHi2N5iAYyTw8zxz4Qm43KSnxZMkyowtCkhAzxBXpSVLP4tASJQSmqrBFifNdlkQjE1GlD4xFJkHeO86nRPQe56b7GV1wIfMBU24XhBhmYES+sUrltqiSGS3lnH+yMQ2TY3AOLyVBmIzKosbaE5aLsxwEl2esFxfs9rdMo6MfBqySGGOpCjNTnTL5VUpoqjzf9CHSthYhYs52VAQlWXpLSDkASVkgZIVSS6ryBASE6AhO0E4T++EdOnao1JNEzm4kAluZLGRcWqQ2BFkgxG/np1SwWlUYqXjzbsP24BicYGGLuR0yI3zF3BB50hHJbZ2c9T/ML45BMIRpFnvOvXoV431FCSpr+0Vyq1UbhC7QVYWMkSAkw6SYBkk/aDZ7wbCJnI0Dpril6r9CqgJbrVgXC3TZUC5Pv8vD4x+vzVLKge8BIJPnNMIn6BwxCIKSFLJGiZJ1cHTiwCSOXYJjWSjvf//p6//j2aXgoZB8/O8nL/MEjSoolmuWZ2eY5GnbgX2/ZbvZ0PnIchhZVjV1XVCsGnShc8AicDi03N1s2G4OSKmwZZGD4Px89t1IcBPLyyWF0lkS790OJQUhJtw05YAvIAZJ9DAODjd5gg9UywVKSpzS7HaBlAKXl2eMXUe733F30+F9xMfI7fUtt9e39x9OICiKBX4SXF2/YewGBInVskEsCpRKPDs7o1mesDp5lhOy5CEMVNLjreBsUSBUbt8bq+cMIpCiRknLcnGONgWJRNM0hHCK9wNBBoSWFMViTvgEVkZEdMTguLo5EPzEftvTj45xTAhVIjRZ4jGOiBQRKSHTt29eHtVIoRHBI2SWRQshgjgGwYRUCSGhKCQhRMbJkVJuF2tTkkSWf0tSEhH4KFBilh1zEMb8erIkGwIYg++zT6RBcra0fPzc8iefCaw1SGnpuztkdFzfRnwUeQQRA9MQmMbAOGTpSx0T2irCE+qRoCwXWKPBD3nvDLmFGmPCzzO5YzHivc/V/mrJDz/7jNVyQb/fsVw0WKNpqoqm0SyWJ+y2G2SSvHvzjqZpKMuKGCXe94x4Usoyb0LkhFopg7VVVtFi4DDM4ghz1wopsLbKNAvnkTLlOeDcxPlnTCn+OcCYCD6htSJNES8y0CT5hEueMEa00TR1jZWJ4MGPAu+zMO7dbkNVNyylYLEoM6kUzWqxorQFu34guUw8PamaPKOKZOX8OHsLOo8bMyBGCjDaYBQUJuvZkSLBO9wcOIGstSgFQiqGcaJvh/sLlFLi5dtrNn2eu0S5wtgTPvjgp/zJT37Gn/z4T/jxpz/lsN/zu89/zdW7V2z27ynKmqqwFDYrm3g3MQ65by4QKGuJIQ9/tTr2qxNBBowVnD5bAgqkwhYVTbOmrtcY1TBNE93OEcRACp7UT3x40nC+WFLVEodmxLL67AWiUhzkgX7vMevDDASa0bCtY/CO373foMqGU7vKIgVzlZBizLMMmXUQY8wEb20UJmV+UZozWinUrOyR++8xgZtG/JyQWGsIZYWol4wikKJj8gkfJCHm6sPHTHguWoHcK3ZFxEuFKBSbV58z9b9nM/6WGBVCWnT5v3D54Uf4GJ/ws77f2s6ufYhIEjlpKP0SG0oW50v6IdCOgZfvvqYqCqQpWCyXqMKwExtCmrImIt/HwPcPtFzSH/n3H3sFKfngZ3/C8+fPGNsdX/27f+CL37xkc33Dh5+ccvnBmg8+OCPGFaZU7A4H2n3L22/ecfX6lu3tgTQEgoyMMbKTESFgch4jE8Yq9ikStKS0ltXlGtOUrC8aQqwxVvPhi3PurnbsNi3htOT0fMXZsyWShJSJ9WlN3RhSTKyWlvftgdvbnnevb9BacfnBCT/54XMWqwrxP/8CyJvmv/vr/x6kpDvs+MHzD7i8OOcvfvYvsaXFWEVdF2hjUbYkAVM3cP36mlW5YNUseH5xkeU2RCShQUgS6gH4MBPwE4H1yZLFsuTi4ox93xJSRGtDWRQYbUgzh8xNEyJHOprVJZPr8X7irNvQdXu6dsfbt18wTQfGccg0g8fBQkBpE2bWEjY6i1CP07z3JI/SAqFFtm2ag2QWnn6Y+5WLinK5oCot0zix225R2KyZimRyfhalVpjRkMqCae9RIrI+sfzZXy75i59VXJ68zbJoo+bnnzUYRn79+Q5PjYuK6DvGzjN2nigNUinwiqosCFrdr2GBwIolVkqCSvhxZOgGXPD0nWOaMhBS6Tz+yQjY+Zp6R0yRuq5ZLhpWiwVaSowtWK8afvjJZzRlhR+GTKHzkRASWq8xxQdMLuuutocx2+AJjUyKRX3CyWqJ7wbS1OFFVuoRCIyuiCoHdSszLYj7Sef/BsAYfFY+4ChgLQAkWhkKY1hVp5RlkWdbDmIQECqEsChpqauasqpYLDI/SEkN5EAXQ8QZSyEaKAzaFvgQM18wRUKMD5VgjNlD8KFvlTequWUbQ8gD/iNP8OjbJiLjrEX6GO4wTAEzRUxlWa2esV4/5+d/+pf86Ic/4tNPPiUSaNsNV9cvcX6PFAFrylz5JMk0OfxMA4khV5nOeyaf9R+9c3kQLvPAOhNAQUqbiaTKEFPC+RHvEm5yWTldDEgCZRE4XWmen9aE2OOEwqqSi/ML5FLTmILNbUfX+yfJ6t3NARcF/RDQUmNUFhkQInNoYkyzGs6xhRfwweUF7TTVDBuP97Xj0W1vDoSZeUvwE0NrISaMLnDDiPcD/SwArE2BNiXEgJYKlSQEEFFgZMCokYoNuAOHw0QSAiEc0gXku4xOfcz5mcfg31meT9yxH8elOYuXAUwUFM0CKye06ClFwMqINgXj6AhToqaGooIi0dESn7zQDMB5HBhTur9CD4ArEPP5PMGr/iMBMc6bQjyer8wbaFFammVFs2rQ1jCOnr7taA8t7a5DCKgay7Pny2yS249AAUIglaQoDaSIT4nBuXuzXBkcQ9fnLN8H7m52dF1HCA5tNVJk7z5jLcaoTLWYHM4HhnEipIQuNIEMnRJKYKTGFA9bSkqJw+4GIRXejViVWJSGy/MzkIIkEmZWc2lWJxil6HcHulfv8ggjLxXcUSlEQEISSOgyq6T4OBF8mGkHgYjPMzUDpGwoLGVE4InJkVJGAue2ZUSkkAP9nPQs64ZFVZL8gbYz7PYeIzSTf2SdnBLEDOSQInudpjSfr8+cWaklyhRUTYEQMaOSixI/Zc/GcZyQxYQJDmsrUpSQAuOQZ+BSgFUWrS1GFmg0IiiSj0zecTXsOBwqnFdoDePgOewm/FTlZ1OI2ScRFBItJFEpXBKQEt55hl4w9uP9whRCsKjXGC2Io2dR55ZlYEFZ5T0thMDZ6SlnZ2dIpWbrogFITNOIDFn6riwKlk2DsSXGllw8O6XQgjTTK7wPxChQWmAKOe9/nrbtSSnjFeq6yZQQFVHR0R9a2m3Hfn8gRJ/RvVNESX9//jGKR8/j9zu+fyXoImnKenrp2EZDUZQ1q2rJxepDlosFJydrggeRsgLJYnFCU6+yQkFhqKoKpXKlEUPi+uqa7XZDLEusqqmsZn16Rtt1/PI3vyHFrBV4LHFzIJzbPckzG29ktJAP9w9ECFkrT8lstRJJ+OQY/fRkMxpdogyCQpc8v/yQjz/+Cf/Vf/l/5MXz55ydnvKLf/gb3l9/zTevfoULHcZAYSsEWS1laEf8NOImhxSemAKTy2R4HyJhVoHXyuRM9njlRIVUGmTKwaf3RJ8r4WlySDlgdWSx0FyeWz66aLi6usJIQShKLi+fY09LxkWFtDdsd/2jgAZv3t4RkmYcEqbRaG0J/qiiIObWQga/ILITh3PTPDOc50oy6xNG0jxXzbOPNCMSg58IfqIVeZZkbcXhcMs09UxuJKQMxDF2gSJSWokRMgfgKDDCoXXLWt7QxQObIQ/IxUyEHV6/ouuH/FDPx3cQbQ8r9DtfuZ+9BZAhoROcLJc43dHIiZXNwC5V14y3jtRHDEv0aYEsDa/4AseUxz7iIcDdx0Pm/Dkdv/gQDL/tSP+kE/rtwjElhrZnaHvC5DBKUpWGcVmwfrbg/PkpZ+dnhBDpu4l219LtDnTdQFlp6lXB6nzB+zd3dF9nCUKpJEYKpM0O6eM44sYRMQxoowm9x8eAlHlNbm4z10wribX1PMOaqJcVprAYq3F3gbYdZi3PRNGUoFX2F1W54s7Z+MMH7fe3aGuRUtJYxemi5sXzC9phoJ9GCJ6qKLm8eMairGmLO7b8jkAikh1n5DTCMIIuiEhShKJYIqQmpSkrp7jcZgzBMbo+k9hlTgRSDPiQbdOODg8peEQM4F2mOzlHGEZO1ivWJ0sqObHZlog4oBAMaXh08yH6YRb8VySyqpSUENKI8wO6tNjSsloXWVw6SLSuGRmYoqDvO4Tp0YXGmBOiz0Gw7wZCgMI2FGVJaZcUtmDO40khMrSR1++vefeuYLOBTy8lQzdw/bal3S+YRpv1HoPP3ogi0w6E8SSfzYa9DxymiXYYHnI3ITlbXyIJuG7g2ekZziekfZYl8WIiRM9qteLs7AwA7z1d1yKkYBwH4pgVueqq5OxknbEBUmOen3NxuuD5xfJBA5qMOVBazg5D+fM7l2XQcu6bBQkuTlb0bcvuZsPVzTVd1zKFA36KeH2keeSxGfco2O93fP9KMErwAh8Ci9WKqqpZrU55dnrO5cUF/+Iv/4qzk1OenT1jvzvgJg+xYLk4oWmWZFHrbKkZQsA5x77bMR0O9LsdyTsWi1M+uLhEaUVwISu9zNzAIzxWSIEtinzTpGIKHpBMo8+6oJGZfpBRVNHP2qMCktTf2SsXqxMunj/nk89+yn/+n/2f+PSTn/Dh5YfsN9d8/ftf8Pd//++5vn7L/nCDVFnNYLjZEMLxRs2ZpJDzS0uMKSiKCkSehfoAPghsUQEw+Sy3FJ0HPLowGG2ydoKUWJUNda1JnKwVbbfni2/2fPH1G1YXH/LRT84pygXWalwS+G5k2Hf3D2hKic9/+xpTLCjMKp9bCrMgcCbr5kSEGeFlECLbVo1jtl0ZxnHWe9W4FLIyw2yTpZSaDV4nwtTTz59FJEGSmSzcdSNKgLKaqmrwY0d3aFF9h3QDtQUjD9h0y4nskCbSlgXBB6TIBqI6kbP476i+/KHju18X5HsSTGCvWsboUO0Kaw2FaOijoOsdabrGuwFFQJsS7RvUvuZMfEKntuzsO9JMIVFzAnacev2TJd4fOx79ivee/+b/9t9wsmiIKVHiODsp+df/5b9CWY1QgjffvMubUEqsni1Zny85/+ASN3a4aaDr90yDZegaPvrBJTFFrm9vs6SWFKxkjZscbnQZrCXyhjpN/ey16bPTgVaMfU/XtfTdjv7wjKZpeHZxzpdfvef6asuLD8/uUcR/8hc/yh8mOBAS3fun9yskZBQYa1gt1iybFSEl7vZb7nZbFlWN7nvqtiOGRN/2jKNHGENKgn4a8OOEn7IofySL6W/e7RnGia9efk3bt4zTwPnzC8q6pFzW2bhXQBIZtMcMq2dWkGLej4iBzc0Nu92Ot+/ecXH+jOeXFzS15eMPPuGnP/0RksT1u2v+/j98ft9hGtqRSQZ8khRlRknmEU2JMZbz8xV1nTVYh3HEKE1TregYGMVEiiXVoskAkdhQ6YoXz0qGJgvuG9NQ1RVFWaC1mRPWhPQe4bZ8tXvH//I/bxj3ez5cXGC14uJ5xdc3A12UJHmK1g4R8hgrBs/kfA6+UmAKez9/Ox5SKj7+8AeEMNL3LUF5iqrhRz/6LzBlFtxApoya15oH0fkEYWLoD/zyP/4dWlmCT9TNAmOyzVdT6bmNeQn3WIRjS1tkndVj9y6mpw+ISLipx02OsR9o2wPDMHBoNxz2ew77PVc3t9mk/d1rZAoQu+/9KH7/IHgMLCnPiIwpaOolVdlgdYmRBUaVlKbBm4hOHpKl0AVGznYnMctsHRekG6esQTllaoOUksKWJNLsJZY1P+9dn2fQRkqZQIxUeXNGPmTnSczVTN6s7xGQM1DDu8AbkbNjEBRFRVU1lNWS1fKURbOkOxy4uX7P27dfc3v7lv3ulmka0KZESJUFXb17UIUQoOZTkIJZWcXMA/mAcLMkk3gQ9JIzZFXI7N9ntM6WKtkSFKUj1kTKyhPiRDdODF7QCIssauSsCZmCZOo9Y/sgB5cSHLqJKnm0LHJbc0bLHl2bpTxe0+O5zO2eGAneM00jRoBWap4X5uAqpbgXIciZV24teT8x9C26zCLOWutMbg1ZDzbFgBbk+3GURcKh0ohVkcpKVo1l7P29h1lhBDE+XqLiW/895nvi6Ze/9V1EIoqIJ+LmViwogsuzZqk6RIozOCTg+okwZTCGtQWVrhhFIM7ArXtVneP7/MEY+G106LcNdR9ONqXE1au3dGXJ5CM/+uSE8nTF+mRBSAkXIjFkpxYfIkqV2MLQNAu6vWToBONwoCgMy1XNYlHgnM/csznTrkqDMxpnDF3bIaXEWkNMOVHrO5ed6CePJM7zXkfbRepFR0Sz3/cM46zQpPJirxYVMQaGvZtbmE9vwvG5buqGum4oioJxHOiHgWEcWVR1bqmNI12EYRzxKUEIRJl5ySGG7GASHTFlUNZud2C/P/Dum1e0Q8voR8CzPl1jreRoVBiRORCEOFeBs39oDPc+pH17oD8ccP1Atz+wLwqq4pTC1jy/uECkkLuf4mG1CZGRnEJmCpFSGiUTMWURcCkswQv6GYgllUKrgrKQaFFSFGua1ZLFekVTZTf1Uk64MiPLpSpn891MYcrPacRqj9ETUtTc3ux4+bKnH0+z9qiVbDvHtlP4ZDOoRh67ZllURan5XLRGoJDq25q8cyItBcpYjC0py5py0WCKIldtIVdtUmaRgMIa/NRBCNkD1Af6vse7gJQx+zdK9UBmlzkIHm2nxJFHPT9Mx7xSSO4DoxYCo2YLLKkoy4qiNFTVgsVyja2W1IsVUmm2t1dItf1DD+UfPL53EIxJEKJgComYFFJZFqsTkIr9vufLL75he9PiDnFuuwm0TvhpR3foch/+eIGjZ5omDrstfdcyTdnSJcQ8gYqkWSTYz5p3OdgopbIDfMozwSQzl0cqjRIaMc+bqrrBWo0tNHVTZA5bXVOUNdPo+fyv/47gcza8aNZU5Sq7OCcYhp5f/93fs7l7xXbzhrv9u/kcB0KUSKnvZ04ZjZRmpFSeL2S6hEIJg5JZu1GI7KTh53aM956yzKad2mZyqjaW1fIMo0u0rqgbi5QOP77FdztcbFmev6A6vUQ1a2RZgAJ/0OxvRjbvD082WR8lAY1WmhinrMs69A+BUh0V3JnRnRJmDlIIgcN+Rx2bbJ4Z0oy2DDmxIFfhWbsRtJYQPYfdHQ0nmLLk5PQZd7dXbA9btrdvaYzicrVElku8bZhERAaH8SPGSs5PCi7WBTd3jn7ytONEYwuMsH+Y/sBxNvgYuv5tAE1OOVSUqFGjnMbtEy7MVcEwYmTkpAJ0SUCxP2yZhve4wbE4e065sKzMD3lXvKJTLZGMPk7H1ph4ePd/uiL8Iz+TII4j/eS43XX89IcrykYyOI/UFmUt61Vi6Ae6Q0caPcpazj84ZV9YDtuCd99cUZU1H/1gxaJWtK1HhOxRp5Vkua4QaFISvPrmPdZqzs+XVJXGOY/SV1y937Pd9gzdlC2aRGS3/RpbFnz2J3fzOMOyWNccxxmoDBo5dD2VNeAfa1FmE9+itPzgk4+5uDynaiqurt7T7nf4aaQuC5QQ9G1LGw8M+45Rq6wv6RJjcIiQ53dH8EUMkS+//C1X76549c1rpjARiHSHO1588AGndZldZpgRmyGS5nUdQsggrRgIMTI4x+5uQ9/3NGWJHyeu3r5n0TScnFrOzl5ADLT7x8CYjKJcLFZUzZpmsc4CExHGcWCaRrr9HucGJjewWtfUtSFFy3K5xuqSs9PnnJ6dc3L6jBgyAGV7u0HpMis1xdyNmcbxoXoNASMVVVFzevqc27sDbmrpQ49CkSL88suBV+803aSwOiBVRvB7IQhSsVytM+1J5rUwHt2OZ93Nq6u3IBP9NGZx7dHz6u17muWSoiyp6oLD4cBuu8OYbD58dnrC2O7p2wPBR3bbPW/kO07P39MsFtR1SbJmTp6P4t+R/W6PFAIzW6oJIWaXiPyMKKWyH6KbaNsD4zjSth3eHRG2GqkszbJg9ewFHznPj3/6Z/x//vrfcHO1+SeexYfj+2uHxjS7uCd8dDif3SKkUAQR2e0PKGHo1kNWRZdq9v6aiMFj1Fz+SklMua26Oxw4dC3d0FPVFVFk4yXnHS7kjCrF+U/KSD9EIknyv5UguVw1np2ccnKyQihYPzvDFJqilBib2whCeaSGdt/yoKgjWK3PWa2fobXh5vo9Y9vyzTe/Z+w3jNOerusymkxkd5GjUC5p3gYfValWlWglZxmkfNOz5UpCiUxOleRNqSwKisLmnriyaGVpyiXW1hR2iTQRpSP1aYGMI4SJTe+o1s9YPjsFq3DJsdt6bt933L47PEJRZtHc42JxbiREn6kkghklepSgGxBkqbT8OTKQKLgJP2nCOCKEzkN/N2ZxcMAYi3dZMSPPlkBpwzh0ODdRz3NDpSTEkVVzwp/+6FN+f7KgK3X2iZxGUtvy5v0dwU2MQYHUkAQiqVmVJ3wrbjytBp/Ex0eyL/cAmnmGqWSJlTVKm6x4IzWVLbPqTnBZfUJqFqtTJjsxFhNVtQQ000azXn1EYwc24j1BZnk7L6Z5UB0RqPlkcpZ7XBcpfTvwie9UjlIKfvKj58Qp0vWO1683tL3j0MFyXVIvCpqqRChFUdUMXVZ4aQdH0xQ0y5Kf/eWPGdqe/tDT9wOkgMBDyGLch43IDvA+0e97RFNkkAgKpaCuJVUlGYfM1ZomT987tvseMwW22z3rkyXGGvbbEa0zydmqBqFNpk75eG8rdH9LYsS77Mn39cuv2W42nJ6fsW9bhmHgVzcblM50H6kVyQfGbcu+b+mniSE6bBIUQrFc5cq4HQaub6+5O2yx65pS1AjAjQND23L77j0yz0Xyup/ngD5keH+YHSB8iAzDOHP6BMvlah4PaJ5/8CEnp+ekZPF+5Ak4VAhWZyXLxlBWsGjI1CBREXw9jxVOmNzEOI3kylDQHQL7aYtkT98Gum6gaztOT5e5U6IEy2WDrWps03B3e8d+t83X1QXc6PBxjzWSk7MFh02NtgNeLNi0sN0HtoNkRFPUFiMTkpnsrg1JewRZINvNDjDOTfetjJQih26DMoqUPFVVYm1FYRV9t6Vt7zB7zTRODMOANoZpylVgmAamoWcce9xU4dxI12Vg4zj2NHWRJdKKihgTznnevXtPShGpJGenJ5jZDsv7zKl89c0rXr95xatvXrLdbRnHiWHo558TBJ8VvkKMfPjJpzx//pwf/fCHCOSTNu8/dXz/SjBmH7Hc0suk9oeLB+M4MU5ZHdxIkwGqKeWBM2RKhAShcvUQUnaQd97hM5ImgzNiwMU8tD9uZnnQOfeLyUaVuYaJs6FloCprispSVAWL0xO0kZgCtA4gPCG2SO2J4SmsoiiaDHSRku3dLe12w+buihC6jMac8kWWKsuFCbgHahw3OyEyvydDsbOO31EjMPhElAKtIMRAEgmNorAGa7LgttIZBVaYisLWlEUN+kinOEGLvHh1N2IWS5rVEqEFbgocdiO7u5793VMH9nBPLWG2FjkOj+fPn7JXmPe52j62mrNYtcizQ59l6pRRiJQfxKP2hdLZIiUhCCkCEqME3k2z32AJMWZJOQFNYXl2esIXhcbLfD9F8IhpousG+nGidYqqqdBCooPIvKbRPw0hYv4ET2CX+ZyedkWflo9SKBQG5tZ1IQSlyrJL/SzUIBCYsgAUQhm0scSUeY1NdUKSjkF2TLHHyXGeA6dHFen8nmk+uWNf5zuE+/SkhyqE4PnlijAGvv7mjtu7jkM3IYTmMq4RMlGV2fVca8lh12dngMMAz9eodc3ZxQl9odEpMo1DDlAmo+8Eie7Q41z2ogyTIxZq5nzljoDSYtakzF0BH7Le5DR5IoK2G7K6U2EZBw+FRBUSJSRx7iREH7Kl2ZNPmp/Rvmu5ur6i7zsQuaqdxpGDu8k3VYm5awIqRTbbHd3Q0ydPJRSNtBiyEP6uazm0ewY3UK3XGKlQCLZjfs39ZoOcE+cpOkKKs1+hz4l8zAHAh8jQjxS2oCiy36I2Bmstq/UJVd0QY3ZwCOFRx0Fki7WilhQFWBOQypNI2WAWmb0YfZjpADn4Rh8YppbgJwTbjOBMicLkFRRCdo4vS8PZxfl8WQR+GDMCXXo8IzF5lquKqS+QwuJCwTil3EVxgoCisNlMQJA5hipKdFSz4L/P7eAYnoDOUkoM0wGNIQSPFcU8UkpMY884DSgt7yvqlBzRK4IbSd7hpwnv5j9+oj3sZz9HiZ+qe8WhlJjVxQ6EGJBSUBQWW1i0zkG26zq+/OorfvObX/OrX/2Cu03WHJ2miaZpUEox9D193zN5x89+vuFPfvYzPvr4YxAy00C+5/H9Z4JSIo3GWEu1qKkWNdZajDRooXOQUBJhZxmupCjG3Bsep4mqXiKMRRYCbIYVV8uGoq9wYUJJgQ89u/YGKQU+jggEWmmM1lmlhOzSPAU/64h6dle3HPYHlNAsmlOa1ZKvvv4a5wZ8HClLhdQJoSfW5zUxPt1Uh9ExjJ7KaH77+a9wfc/JssKHHu97Mg3EIo0m+pzZphDvyeYxJqy1NE3Dpx99gtWGru8yH49EUwZc8ExzWX+clx3/NMs1RVFjixolC5Q0SATnzy5Zrhs+/MEJLgz4MLGYHLoqKVZLDv6K/W7PV5+/5NXvXvH+6/eZ+zcfeaYT5tlqejRPfTApzuabWWBARpXnBTIDQGLK6jvjMFDpPFecppFpcgghsdYymVk/MSVkymhTa/Pic+2WGAIKxQ9PP+FEL9hdOw7xis4ZxOIFOkRKEVidLtj3PfS59eWc4247kIj003A/E4Y86rnHYj4GZH7rmJkGeekCYpxIfYvrRoyWWCPZbDc5yw8jusgahvu2pV4sWSxWmRQcsg9mJS6w1DROcbt7zba7RjXPUHWBXtUcuMOlgYkut/2PiLv0EAuffIbH/y8EH32yRidNNyX+u//nP3C3belHz8mzFSenp1RW4lxkCI7b/UDbjnS948sv32MKyV/9y0+RIZJ8QhvFyWnDYlUAia6b+B//+nckEdFa8OykQqvI9mqLazOysT14BIpFXbJclWx3A9451pUlArvNnql3bJuOy4sLyqphua6IQc5VkqadPPv+6Ywppkjb7nn5cmLT3LCoa/ruQKENWipkTPTjwL5tCSnPMcva4vvMQx1ktvWJUdJtdgzOc9Md6LxDGE2xqDEiW7wVRcU0eV6+eoua9afG5Ihz0hxjuF//EpkVplxAaI1Rgnq9YrVasVqtKcqKhKTrHcREeGRVloF2jogjJcX13RuGwXNzO1IWS8qy5vT0DGstdVPw4fNPqaqGqqz5+ouvuLu9Y7c70PUDw3TF+7dvUQq0ldx2Wy4un/Pjn/+M89NTkk+8/uorUgKtLDftLdvDHenL39DuDWMH1+8t3SB5e1Uyuix+IKUnuowDGN1AFBZVGtwwYxGSpy4VQaj7ByqROPQ3GG/p+olhHCnsgBYl09Th/YSb8t4tlULEDPAbXV7PwXmcy6Ca3Vbxq1/+A35u666WDYvFgs8+/Yyzs3PqusEWGTGvtCKQmLzHx9wJ7KeR1+/e8OXLr/jN737L/rCfDRIil5eXFEVB17ZsNlu6rmN1es755XOcD2hrs+Hz9zy+dxBcngnKlUabgtVpTdNUWTxbCJTIvKZEwo0jyjtEEPgxMs1Q6DfbA9JodGEpThuiShRK0JQWEUoEAjeMbG6uiSmy2+3Z3W0yPyYJvvri97mf7/0MRMnWIdv31+x3e3bbLVPwmP2W66t3+JA5QbbQ2VKlCsgqkfAPrSoSt3c3CB2puhLXd4jgidFkVGqIBD+rjszzMiL3SuXwAHLRUrFqVjR1w9kJdH3HNOZ2iPITUk73SjnZUiUPlqtiSV0vKcqaoR9QUlCVhuWioq6KrPCgJdqUlLpAGEUisr255ebtW25ef8N02CHC9PSGpQTkbFg+Gjrn1lBACZ21NWeUR5pnAnlWnYOgCA7hJNqNc3A8+nUlvBeZrxnj3AXMWkVxJrcH5zOoIUSMXRCwXO16RnsAuUd0HZWYOGngk/XH3G13yOsdh9uJdoj0k8+Q8z/iEH3fFH3AndzHmfSoKpy/QBKBJB3Majx9FIxuxE35oRWDRqgRSfbhi6Uhzs7dRiT8eEfyiilA2wYOLVQWtANcVvxPIuJiz+MS9T5Yp6fn87TFm+h7x/lJxc//7CN+88Ub+Drx5tUtr7654eR0wZ/86aeYWmHqlJGcooV4IIRsKdPu+/wcxpTFnbWmkJa+H/FxpO1GFivLcl3y7Hm2zBp6j1AyJzyzw7eQWeuyqiwnZ0uiz87m0+Q5jC3TlNdIkgqhLEM/4L2fpfPUdzLwzHdkJu+PtD3cbO9QQqJyE5nJOfppykhrLZmIhGHMlZNMuCjwQZCEY/CBw9CD1jn5DmCtwVpDawrGEBmmATmDmFxwxBmPkGLOoERGmeRlo7JnaEgxzyGjn33rJoRXCG2Rcxfq8bM1jZFBBVII9L1jHLN4eAgd4+SRMivcWKOxKhvqVmVNYmS5Lnnx8XOaZkVZ1Xz92y9ouwNt39L2E2Z34KuvvuR0eUJd1JlnKBJCjpysK6T2LMqaVVPhZImShmEI3NxM8/3Jz6kPWfUnA1Fi7ibZPGpQ1qKlYPQPZHlSwoUOHwe22452HyjLhrKoyYOqI6guB8E4ozwTAmIOgmnmDk9jz9XNlkPXcXN3x2rZcHKyRqvsGqRnRHqMEec9C5VJ+CmmWbrRsFqvOT0749nFBcroe3Wbuq4xxsxuFpZpclw8f8765BRtDMZYtP5fux0qYH2hWZxZtMlqAFXRoORDEFRSQIyM/YDsI4wJdXAMbqD1I3dTh1ASU1gu0wfY2mJVoikMKpb0/cjYdRy2W8Zp4nBo2d3eotH4Kbthe5fbc5Wx1EXB2WLJ5u07tocDd/sdcb8hSUF72AMZyai1RltJFSWqDST5EARJcHt3RWCgLC2NKaiMJkU/oykfgmCKghTy7xljYH4wHgfBpl5yenJKXS+4u73lcDiwP+yRMlsVKZWBJXVd03cdCUFdrljUK6q6YRreo5WgrjSLpqQoDe3hQLWssEVBqRVeRFxw3L674uqb19y8fonv9ujv+GelGWkV7+d94SiAHTxSSZBx3qBnJN3s75IQsxZi3qv1OGSLJa3ur513cXYJyQ9ZnF/3SMFwQ26Nx5hQqxPGBHe7jqnagWpQhz01I2eN4Eef/YDr6zuS0rza3zLgGX2g0PIPtBL/WPH3UBYeK7F5RDfrHwaSBBFCphqQmHxur+x3HUEnUJbGWopCUZWaOA8UNZKpPxARDHHJvvPs+8w9DCESnUMVCiUNKgjC027nd877299KCfb7kRcXmj//6SW/+PwbxsnxP/373/Dy8j3Ldc3P/urPKOuaWhqmbqDQEhEmXJBEEn2bPQK1VlTaZoPeErrRMbpIN4ycPCs5PWs4f7HG+cjmtssC0C4SUw8yu8PkIFgibQlB0B4Grq52tIOjH0NGqyYBQtP3I3F2cj8KsD8+lM5Asez07olj5Ho7QxVjNqXOxCnyBqgVkxCkYSK6wKCgiFlSzCUYQ6CdJiplMEKhfXYDqYoKYwvGKXuMSpk7ICFk7u5R0uC4gaNm1wcjESqLV49+wgV/j0uQXqNCHst8OxebhoQi4rVnGGbH8xBxbkCI3C3RGrQGP3VYaymLksViyWq54ud//lMun3/IyckF/2acePPmHdtDTz962B/43e8+55MXH3Nxdk6KExAIIrFcL1G6YlFUnK1qXFGjtWUceq6vR9woSTILkBzbsMrq+anwaK1QQmCFRKaEGR/5r5LwscVNibvNljcvt1TlgovzM2xh7luhUWSEaUbJZqxHDBC9J8U5CA6S169ec3Vzy9fffMNqteDZszOWy3UGy1Q1R7rc5F3uNCqFiw5kdtQ5PTvj4vKSD+8+xFqTZdrI+69UCikl65if+xcffszp2dm8T+l7Ctj3Ob7XTwoh+N//1f+BFz94jjIl0yRwU2LzvkWkrG6gVc42xr5jum5J+57p3R1XY8v11PLOdxAjJiY+efch6/WC88tThqGnHzo+//3XeUBN5rAEHxA+cvXF1yQXsEojYhaaPVk0+KbBnJ5w2G7YdS2vr98irEYVhovTkye8PW0Fi0aB3DO63f02JARcvjhlta4ZuoHVoqK2BcFNWfEmmdyKSRmtWlXH6je3eadxoiwLYoh0bcfb1+/xQ+KHP1yzqFcYZfEuIaVDqQo5u3JbayntEiEVJ8sLlqsVddNAmkB4tJnwbkcMkpvbDet0xkIJtC1o2x1XN+/54j99zt3b19T0PD8xFLHhb8TdfSg8zm7jzAkTCKL3RJ99uVAqq65LdV8puhnVK4TIvmIpZiTv2KKDRokSMdNRnPNMQ8/Ud7jMsGDoRCaxuqw9WVUVVVVxG1t88HR9h9+uMAFelEs+ba75dD3xpz/+lP3lcy5PP+A/ms+5ud3QnCqUT7Rdz9/9XvBP6eGme1y1eBwPcyIwS9YJFWHM0nZCCdp+i3cBbQzIgE8939zu+ebuLlMIBPddh+pkRbFoWH9aUJ4LzjB4u8eLRBCJNOUzfChM8/W/b4d+55l6fO6Jr17uqG3Fel3yr/7yM06XC379d1/T7Q589duX/Hf/97/mhz//KX/1X/3n/OS/+M+IY8vNl59ns2YpELrILgRCYE0gm4uO3HaOaFoWq5LzizUffnTB+mwJSNanZ7x7eUsMAyfrisqusr+ntEwh0fmEkorFYcjV0G1L5yL/8Pe/5uMfPOfTTz/AD9nLsiwMVWVYLOv7DyeEoCwrjLGzZVf+4IFs7qqMzO7yRUFZlVzf3OV15RLjkOk8zemayhSU2tCNI2EcIYY8H1WKVZMFOupFw9v3bwkp4oJHpvx+yhgKVaKUpKqyW0tVVSyWi6zGs1hQVTnJPFmvqZuapm4obJmR4KrA+zyuebzWxi4RJ09KYRYZKDg7XRFjpnBl+kBOAja7Dc5PDH1HWVmauqabdvzkxz/jh5/9hHrpOBkl+4NluT7BFgUpjlxffcNh855CRZSKaBW4uzvQdz3d9YYfvahYrX/A5BL7LrFtBdqSfTxDpKxqpBSMrs8JbkwU1ub5o/P0XU/bPijGpBS5vntL8IJDO/D69WuqouEnP/6E88tzClvP4N+H+3tPrUrh/s84dgxDx69++Z94f33Du/dXGGtomobtZsfvf/87Pvrok2ycAIQEPzv8CWVZ4UNgGkcm52i7lrqp+fSzTzk7O82YivTwhKWUCDERYwblXV3f8u///f/I5uoNb15980/sGA/H9wuCwNnqlMuTc1Ca3W6kdQ8abvewgJQy6jB6fHBshwN3Y8vGdXRpIvmAcp6bmxuCH1k0BT44ggu0hwNK6bxxlmV2Mo+wb+9ws/J5ClksewI6H5BEdt2Bw9BnyR7ijATMG3uW9Jk39qQQUSDjU005rQRaZ8BBWRpKY+mDy0NlKZHS5KzKOYwpsVZhdR7sOufuq2EB+OBxfmKaHCmRRWB1QUKCyHSJrB6jsLZCK0NZNhhdoKSiKGyuw6Rn6LNkV9e3lGNN4UqmNrDf7Lh5f8P162vazZbSKOrTBYUuEfIVj7fcGePC0TE7pnAPMU8xA5tyJsdDB+/I05mdsxPgpoEYFII4y91JvJsyWT54Zjl14hgY+h4fPGIWn5ZCMIaJEBw+DijXUU4HTsdbzs4SZ+uaumlILrI2iculxcaKIizwvSdLCHy/I8fBNH/mR4s3Za2HqEBYzcxyQJoCJQJChCzMi6BO+v53Zczt3sl5FClbidmepBxSRITMn/t4LeffevLc3LdDHz9P9xd8Pu8E3TCxOwzc3XUsFhUvPjzlxz95gZAJN3q++u03KFtx+dGH6I/OkDFrOS6XFVVVYKoFUUqiEES/J8UJH7IWrDIqy5RJkVHVc5IgUoaha6tJMhvjWmNBKKKPaCJFoUnBsFpW3B5G+pCl145i9W2f7XmiD1SNgEfpihCCi4sLyrLMSenciswVY9b7rcoKYwzGWobR0bU9h0OHC/7+usYUZzxARnZCmg2dA0KQ6T/TSAh5plhV+TWVVhSFySA0q1k0C8qqyvqWywZbFDSLmqIsshh+VWKtpTA2r/O5jxvF06QFyJXPfB+lyZiImNI96TuGeN+W91EQAjh/HKD1vH3/Hm1KnPMM7R0ujDQLQV1ncJI1EqNBq0hhE1onjEow9HjRsah7Tk8Mq5Xh+nrL3XZimCKpyOYBjw22s5mBwiDQMnu9pgjBp2x88Oj58WEkBDHP/xyKkfZwYLVeUBYmFydz9XcMgsyjEkFCzV0h7wK73Y6uPWTaA1ly882bVwgp2Wy21PXiXmjfWktVZzGRYRhxzjGOA13X0rYtwzjkKjQ9SGgSU+7qhMShzxgSKaDb3bK5vv4WL/ePH9+zZhScNmvOmhNcDBzcQOgczK0CIeSslRgRRKJKjDLyPnTcpJ6tmJCFJrqMVLy6vmbsOy5O1yijkQimaWK1rrm4vOTi8gKZoNvsed8FWpewSjMNI0Po6fcHuu7A1f6GbXD00eOjR3mJUhGJQpL7zYfdFqUF61ODteVsRPmwokNwiOSpCsOiqahthZsGjFSzn14mHh8OLVXTUFUF61VFEoIQA2ZW2SiMRmkIybPZbjKBVmrKskF6h/IeUxT3KgvL5SpzBYtlJqDGRFFZ4ixNdLu5YXQTh9FjuxpVFRxuO96/f89XX37NF7/+GlzLz3+65PnFOc5JlPzl/SaUF2rWTQzRzUoMeVDug0eFgI5Pf/ZxIMQFkhQgwuzBmBhbhS1KlNLZkWPqCGEkppwQ9H13P+tcLtdYO6O93EAIEzGONG7HehJ8MpR8fHLCBz94hi2XuLihOrznx42iNw2bWrG57Xgdv60Y8zjY/ZHG6LfAmSBIShKtRJ3UpDEQxkB1tiB6h2s3aF0hteW5tZnQnwRD39N1I3d3LWZVI9eGvrh50k4XSLKpav5SNnlNTwPfY9Aoc7v2W8dhmLi+6/jiqzv+5b9a8qMfnvNf/1//Nb/+5Uu+/OItv/ibX7O53hGGgd1f/YTCSvbvX/PDn35EvVqwfHaK0AaU5O7tnmmu+G2R53tjFLRjYNtN1L0jxcRwcGijqGSJGCRSW1AWJAgi0meEskRjL094u+nZu8TydE21aJDGcLdpCc5TGs3FRZx5bfkzKaX4y7/8S1brdZ7zaJ0rJClzkJK5FXfkznrnub664ebqZu5KMKONw+z+/mDjk6kHCRcd2/2WtN8yjj3GaJ4/f55Nc8uC1WrBYlFTVSWr1eq+EqyqEmM0xppsgzbfwCN6lJi39UjKWr/fWmohSLTKn6moShKRu0Pu3mSNYD0r8EgKk9VWytqQCLiQ+Orrt7x6dYXRf8uLD2tOVg0fXJzTlAGrE1YXLJuKpiyoi4BRAasd+51hqARN4Xj2rEZqzb/5my95d+s59Bn1rLIqBQ53b/NWlRkB27YtbgZQpfA0WOYF7YgxX3dBHqHcXl/T1CX3vTX50PoWUs5Ar3ydrNHZpHkY2O93DMOAtSaPTELgm2++4d2792hTUNc1RVXTLFZcX19T1zW2sEzTNAfBEefGDMhz2VEo+DCbpOcz9j6jfLeHPiNWg0fh8ePwB/eGP3R8zyCYuNl/QbntiNHi0Zgyu74HH2c5okj0mZA9Dj3t2HHrOoJVLKs1P/vsJ/R9z/t373n/8iXdMDBNnkVVonVBvVhQ1TVFVebvDQNXb95y2G+YxqxwMU0TY8x9++SzSPcoIYhczZEScXIcti3G5kW4WjZUteXDixecnDRza+PfArlaO2z3lFayXqyw1s6IpXi/eTaLKoMPZC7b22HEp8DoPEKp3AIIjiBg9B3aKQ69JaZIYUqssSitsUdFkhiz+3bMQrN1aYkp4JOjbfd4P+LCwBg9EYlZ1PSTw13d8u7dFa9evuI3v/qc26s9InX87osdKYGU5ZM51GOx8TwLjEQfiSHPBY/k2DDP8tS8cR/tlUhp5mfOdjvZTwkQGHMcemWYoxSZMF9YA2kG/2iNTIEwDRmeHiYm12HtnpMo+KldcpEi9dSjuhYl7rBrwcpYqkFSJIcoFHsjv41xuUf4iTkQPsBQ/vgR0jzbWVqEFnmOay2kgCwKtC7QylAXBqXzmozThPeRizEwrEZcMTGkMZ/P/XN4VJB5lHnPrdB//Il6WrFfXK5YnTZMwMs3txkmf75gfbKgrmsciTdvrxj/7d+yubrm8nLNn/zoEhnADxPddoOxBmM0fhhzWzEkps4zdRM+JF6/2bHdj/gfPkORGNuJ5x89p1421IuaEGZVESkQwmc6hRD4lOj6kXZ0tOPE/rDHvIv4rqXd92il0LXBh8Q0hfu7IYXg448/Zn1yipjXfkrZaf0Y9LquY5qmrDDiPcYanj07Y5q9C/M8O99hWxiM1RTB4OdZ9G6/pShLiqLg089+QFVVrFarHAQLS9NUGcVuNEWROy4ZwCNmr1HBvR7xzP/N/z1Keh3/erQKhcDWBmOzM3rvDvjoGV2HFAVCmJxwOp/nsR33QJXHSar3WUz7dmc5P1viXOQnn1hWleTZumLVRBZ15GQpKKykKDTDsGAYFdd38MXLW169P/D6dmKzG9gfRqahyJ0moxhlQEiyfFtUEBVu1zGOjmHIPp3y0aYhheDsWU1/8Gze95QWko9sb3acrG4wKsNAsytOxkFIkYVT4gyoU0LR9wO73YGpH7Mqk8yFEgK0BGIguoGhDZnb2bVMQ5tVbGTWhr2vqJMjxolxyNrI1hR89oPPODk54WS1YpxG+q7nb//+FwxuzALxcSTEB9GGf+r4/mT5MOHDBGRoP4WmsAmPwyeP0RqjNErKe75fHyasbqiamvPTU9qioG07EGJWox9oWGCtnd0lKpRSOOfohoFde2DwEz56UgSXPE5EvJw3P5V5XXbWv0wBRJIUJqPFyspi7YKmKTk/OeNkvcKN05PKYuwHpr5CNDN/7lgR5YkaWst7RGc/cwb70eMDIGQG0aRICA7nB0anGcYOo4u55WNQIm/kRwm4DE7J2qhaSXzIqKpxGPBxIglw2a8YJbPxZT+MXL295v2bK67fXjMNEwLP3V3HzWqP1u5J+R/nIBbD7LoRw2yMnBOWB3WVSErhfhE/jNKOkmhxllrL4ILg/Sx9Jnjs7iCFwGg9tyQegFK5/RoIISMxVRgoo2XNgOwC413PoFvc0BOTzyoQSGRtGBvDsjFP52eP/uQzvs9XngSWh0sx//IsnRZsQniZ5e/IljVBmiwIICRRKqTSJG0RSebW1FKDvc7o0kf4o+8E3/RoFsix6/hdHZvvxEchqKoiOz4IwXbT0hlJ2dRoq1msGqpFyTQ6rq9vWTYGTcD+7CNiSIzDhGw7wqgIWtIdOqbociWfZuCWUrTdyO4wcrm0FErCrMmrlMDagn7ILVY/k7NjCPcBYhizTVCIkeAcbhyZZlEMbTTaGBLyKYBECJqmYblc5oRr5pflpCoRRLjXEXbOZQmuwrJar+5FlDOtgXm8oOYrKphmIQhrLVVZUtU1F5cXNE3DarXKVYUxFKXNYJAZSHFM8tKcwD2s9bnF9j06aAKwdYE1mpQCPjpESBgrs8QZCufEQxIa/NzhCffvkWZR83GcGIMjJslyceD56Z6mkJSmpikVy1qyasDaiLFZhiySEaBvr/d88fKWbsqG5c4NCAdRaVQsSOQxkhMSh2SKCdcNuNExDRO6sE9oVQjQdv5jwBYSl6BrO9rDgaIQs/MMWSx9DoLZHDd3RMoya9QOwzg7RcQH/rE4NuHyvuTnNZEBPD4HwOPDTJ51QyAJxzT0aKXRleBkueDDywsuLs7pu5b9fscvlcCLbKkVRKZ/fd/je7dDX6z+lM/OPkOakmlMjFPgfbxlHEamacrcDWMx2vDl5Nj0e1rXsyxPuDg5Yehb3DhgNZhCMg0TX775hnJVszpd8xc//zOGcaTr+zz47Xp67xm1wJWKYRwRViJVRV0W2aqjbnj+4Qc0TR5kExMiJeq53bFYLFguG4zNjudKKrabLfJeMiax27UU1rBqTvKQOGbdz+MDOwzDPLsr0abC+ci+7QhREaPOVi7BMeGRu1sGN6KkpCgLbFJMPhJDHuBm/dVsQeTHAacUOjmc65j6HbvdHaYsefbBCw5X7+j6HtcdUCILiv/iH37B7fUt+82WSikEhq6DX/zqNcOYVWKOx9FSKjhH8FOmMiRyJRjSQ0+f3PrIi9lwZFEKju3TMHMJM29w0nnGmtL8fkkAEilkzoyPJzCfSyIxuYF+HDi0e3SZM3QOHb//u1t2f3/g+XmDthJpJYvLJWVVcP6DNScvlhTnxfwwPBzfjm/fPtJ3/i8HokCkVVtS0qTBMmwO+BBwJDQJJaBqFrNQuiJMgmpVcf6Dc/ZpwxQ6BHrGMj61d+IPVH/fDpIPgfG7c0IlNdZkib+bN+/Ybfd88/aOz37yEX/+Lz9FVorrq1vevn4POhElLE4WbLuRm8PAejkS3YSbet5d36ILzfOPn7FYZjWVjz845bdfvOf1qztWQnD5rOHTD9eMXYcUkecfXjDsJ4ZNy/u3G3xMxCQwpWaYIlebAyGmHFiS4myx5KMX58jC5GsbQ7YG692TG3NMph5fAynntSIEi0VOgsuynE1x472AwzGIyJmSVBTZh9Ta4j6oHWdJGeyyvKdqHHvh2aiVp+fznWTkCPTIs63jrDzN6+ZIC7r/aSm5/PAcYzXjNCBkgxQJJSQpaGKA7V3HNI2MY6SfEwoZyMEwRIahZ/CeMQR8t2RyicP2hsPNjk8/bPjomcOeLVg3FWUZCMlz6Cdevrrh1Zs9/6//9zv+42+veXvX85M//wHWJfR+Yjwkki5Y2Ro/C9tvuzsOctY3nhOZyXt0zNSX+8uREneHWwSa+lSzeFZy2Hqub7ao17A/tKyWVa78pcSoXCBopYlJIFSu5IZxyM4Zc7fLKEVZlCDy2CuEnHzFmA2LExCDRsaHZxWyVGemh2RbqaIoOFmt+dlPfspPfvwjLs7PuL274er9e05WdZaH7B1WCx7Jgv2Tx/euBBd2zbI4AaXR0SHClB2X5yFw3/WkIiKrDDmWWufFljKg5fXrVxn547P9URSC0XtutzuQikPbc2hbtrsdg8vqM9vtNmccQrBYrTJNoMwXYlHXnJ2ccHF5QV3XlEWZHzAEhc396nvI9tzyUELdzyGOz0DfjXTdiJsC0+gxKnBESKaUmMaRZKAocjvFFAqlZ3kkNzC2V3jf433HOA0gBYPrabsdMQYKVc2ISoU1BUZrmrqkKEqs1UxDi3Mt4ejkrCQ+wt1mz2a3ZXQtyUfc6Njc3jH2PUqK3G6UAisXDD6C83x3250D2iMU1z2zZ+7w5M85EyPirO+a4uy9lrlVQma4h3MRN+V5UpaF03P2K+Z2q5u3k6yOEef7r+YHRWtLoy2l1Awu8u4w8Xrseb8NKCNQBZzeTjRNwbOzhrJQtO343U/0aFdL4tsb2nw8CZDp/gtRJLABsRoJh2x87HxWDkkxMYxQSEOhNM3FObqWtOIan44+lMetURwxON96j2N18fDvh58Rf/BUU0y8/uaO2AVW65KyMKiThtt9TzuMvL2+QxjBxQenPP/ghKkPNE3Fu5vd3GoShHGgbzsOuz3vbjY0q4rVWY0tFMpqTs8WFG82hJR4ddsyxIS0hsWYWCwCtqrZblp2u5bJebxPhECuDn0mlD//4AKhDOtVQ7WoMaWlXNYZLTk42i7P7R5/wqMA8r284KPZoNEmu6XPYt3ZMu1YlT0EnuwmnjlhSqp70Iu8X1caZb7lbHBsAHx7WRxnf+nhu/cBTmRQyZP79gcrw8Q0HhDYWe4sG3drAVmNJbGoIpSaxIIpZsPgI60gpox63uw6NtuevstdlW6c+OZqZPITf/+rb9DynKZcsR8C729bfv/NHV98ec37q5Zf/nLLXRcQKEI/IUOiMpqimSXskiMGR0geoRLIRJIp0xukoTENpjSkfnyybUQUSmiUFQgzkKRjdBM3mz1dN7LfthitsUbdm5lboxAq35eiqpimgXHqyYL7D4nKkVs9jnH2f3y4/ilkCpOa9+vjI55g5utmrdpxHHn58mv8NPFNU7Hbb7nb3LHb7RimgZgcZWmQ6nuU9PPxvYNgZRY0dpm9F7Qk6lx1xdk4dr/b4asKrXTuNessKRVnt/f3V1ckBMbaGZsimHzgdrtjmBybuw27/Z67zYZAIsSIc46mqqnLktV6zWLR0CwWXJw9Y7VYcPnsGc/OzijnmYBSKg/dZ/XxGLI3XwhZvkgKOZO+79cywzAx9BPOBdwUcPqpFUxGl0qKIkO9pbIUhYG0JIaJDSPjkH3EpuDAiZkUfMgZTxlRwqCFRdoCYzVN3eSNQAim4YDzfQ6Cc7tm8oHN9sD1zR19v2UaRqZhZLfdEn2YW4956J5MjR89YvoDPfBHG8L9Z3o82jhehhnUlCWUMsHe+em+PSylut9c/Mz909rMsxWD1orgHVPwuR5KGTR0RPfpIiNijbY02tAojYuJ287zajehk0eoiDKJs9uJZV2wv/A8e1awH4Y/ivL6xzz7vnMpjoFLJIQNiGUgFnleGgfPMGTVex8MSXqMLqg+K6H29NzM5rq5PSPmvs533y89eqfHoffhfB/at0+rpbev72AI+Knhs09WmSu6bWn7kU3bY0vDs2crPv7wnHdvtgQfud4cKAuN0ZJ+Gtlt9mxutlxt9px4zzSO6KJCGs16XVOUhpASb7c9QwRdWM6cYHSCk9OBw2Gg6yZiTFnyawgMPuABVRScP8tKH8SY5e1KS1mXeYYTJX7bMw7uu0H+PsbM10wptNL3gKz7ltgjoNbxEsnZukvKzP06Bj45VyHHdnMi3c8c/1Dk+ja46un5HYPeUdf1qAv8x39+Gg5IMZsX2zKjxMkeiIhAUya0Vhht8ELOHGmTn1ng0HZU13uk3nPlBtzkGV3i3a2nH0Z+8du3fHAu+eBCshs8n395y1//7St+9/sbNpuR25uRatFQ1SVhcIgYKY1BGQvIHIxTIB5d7mXKHuYejDVUywW2UDh5nHcekzcN0twn5EnAFBxhF2kZOFhNYTS2MJRWYbSkKLIwQFGWrP2Em0amcSCRkEpgi1zpH+egzk1PkpBjISUQCK24N6kh4mfwztF1YhgGXr58yebuDi0FXd/R9h27/Q6XPKhEWVVo/Y/tBk+P7x0EhVAIoYFIWdYIlQmL796/47e//R0hBBbNgg8//BBbFLgpOya0hwNKSk5OT4hJMPpsZ9NPnmGcuNvuCMFnIdd50ZczVHlRN7y4fM7p+oQXH76gLEvKomTZ5BZKXWSEV9a3y6aeefHOm5TIeohKJYwpkVLS9+OTByLEgPMuK7y4iSpalJpvTPQIoWbjyA4pC2yhWTTZFibPIwy7/RW3G0HfH4hJMIxD1tWcq6DCNhhTZCKoB+01hzlI+qkFGUAEOjcxti3D9Yabqxs2mw3XV2/p2y57vO33SMBISSVLXEgMXY+XGv+tLVnpAikNjjGvs7niy3SNrBAS4jHwzRmZzC3QFDzjeMgbkNZEqe5nPcaaHAB1FiMQQhKTJxHy/CGE2SG6JQeMCKNGSait4CcnS368XPG8tjx3JTtV0flE5z13zvH1yy1WSi7edXxwanFx/BaC7Xj88UzvyAL4Q78jJFAIYiVY/FmD2wxsf7nl/EWVZyTO0GBZSEN6viXoQPL+0fvJP/LiD7Mm+WgumN/1obJIgCLr8N6fVUrc3m5I40C7N1w8+zGrk5pnz0746u9+x+e/f8P62ZK/+PMf8S/+6hlGW8ZhomsHun4ipcTJquL0QnB6VvADPsjAMDQKk9u8RUWKgrYdue4HbtuOwzDxf/k/f8LHH53TrEq0KVisckX57u2Wl69u2U+BetnwF3/5GS/m0cP793eUVUlR17iYrZjaKRARCKnuP7fgQc3FH4W1Z+LkMeFTc8fm2B4VZDRjmOeRD7M8eT/Ty8Olea2mef3OAJanXbBH5cQfWiPpOHuS9/cB0oOZK8e3+q5ijO9v8dGgpKSpLjDKEoWgm6YsIF9ohExEIjFpRJQEH+8TSq08F88qVouSYfclW9fRdR1JWkIy/MMXWz548RxbKf6nv/mG3311x9//5o4QAkZbLi6fURYJaxJxGjDaYJuG5XINCMbeMW0dfnIPus0S8JGYYIoeIxVCPe4VC6pwQpwEw12PCRWVktTlRJiF7IvSUpaGqjRMY0/0CaRGGUkiMk0jbdey2++JMVIWGah0VId52Htzm/xI0UppHr0M4/3sNCc1eW5Pys/TjsTdze1cReYxTUyRpBL1ouT0bM0HH58w9o7f/t3r7zXj/f7O8hxL07yBOu/z4klZp3K3y3BYgLKq8LP7wzCOyMOBUll8TPTDlIelQmCNAZM1I4Ra5DLbWppFQ1mUrJdLLs7OWS2XnJ8/w8wzR2sMRhkKa7O5p5Rzy0TeD2GPmVyefeUMUc5eVE8ugM7O6m27Zxg6qtJQmwyKkEne3wzvPcPYk4RktVKUZQ6EMX6IMpIpdPMw39GPE0oZJApnPcJPCDGAc4hBsNlv8LPkmEyOJHIQaaPDo/DC0h0OdIeO4CISidUW6hoRYyYuCzFX5QpTGEqVHlpBMG8OcfY31LmG8YGo5kU1/4wPjszpyy2Z4B3BO9w0ZWUNIRApzsEzZ+XG6HtTYY5GujEQfYAYkClg1DErFxRWURaaVWMxhSTpRKgk5WhZpRIzRSphWajE1raImGhKweiyU8K3M/J03+565B8tjpXXQxVx/MZDCywRPRAjIiTGg2fajbgxUsXsYRi8IFlBKCRxdiD4Tkn35H+fzgLvgTFzJH4aDB9gM0/qSCFolgVVZREqW3ohJOtVzenJktOTA/t9z93djqurOxZ1gRKWvu2ZRkeIUH9yhmJChi7PV6XMCEiysMHdXUvbDjiXW47WaNarBetlRdPkimaebnC3H7lrR3aDw9QV9aLOfLqiwFpD1dRorUjMfFmfHeC9z6Lrj2+XUjqbNsvZuiuR59Pz5iekQCJRQj10HrKP03077VHBwNNdLT260U8rmodJbVY/evxrGama+HbVeA+O+dZdFo/W0PH3q6rCGomf5rajF0SpZrH6DHSRUmaOZlJzMI1z4IUYHEZpbKN4frmkNBKVIr1LCBFou4Hr25ZX7/a8vTqwayek1pSVpiwU63WFFg6JJwsjHIXBfU4YFCitMBi0tVlUAUHyGRiYZvnErBL18Jl9D9EnfJ9QSWNEQEtJkhldffJsyaKpWDYVXbdHkHJr1BazXJl+kGkUWTbPGHMPdDomN/l6p3vw1fHeCXFUGRKkpHA+ryuOoKWUiN7PDa7ZUAGQVoCEotQUlX4yh/6njn9GEMxyUJMPWZpoGrPDsDForbPH1G7HdrvF2AIhJYFE17YMfU9lCpwPbPctMQSM1qyWDU1TUZUFi2U9K7lXnKxWVFXFyXJFU9UUNitKHBdicNm8MQNxHi56bpGoJ4s2D9gzqCPDox9pygkoK4OSkdvb95ydLClLxWLxjDwF0/NsJCtw7Pd7vI88fy6pqibbMJ0sqW5rXMqeV123Z3/oEShIgqIoCXFgmHz2RxtH7ja3SJH5UouqIMQJFyccAWVKimbNbnPHYbtHoKirBaYRpOjw3uHdkNGeMmILSbEsKf1TAEAME0kLtIZaWTSCgY4oFElJvMyctmkaZseLfA2nccBNE37q7/X9ULkToFQxZ/aW/f6QYe4+kGZHYRkjpQwYAroQGTwEXKxrTtYVzy+XyCnRyYnDoqZMJZcmMvYRszSUp5br/Y7oHKV3vHk/sOufbqpP1ae/m+al+1Ao7tvAUmbKCjHie4/oA7L1bH93y9R7piBoaiCC30WmE5CNQgwBqSPKgEh5Mz2+/h97Snj03ceh8fHfQjy9V1IInn98wrKw+MGTso04F6cLfvqjj7DW8t//m7/l1TdX/PKXX/Bf/e9+RlVb7mJk6CZclJx88BmaljTdsW5y1t33UxZB70a+/PIdN9c7ptFjteJsteRnP/qI5+dLlo2hH/N8dBg9v3+54WZ74Lqf+NMfvODi4oTFokIpSUTQLBe5hRkCw5R/LwbPNIyMw/AoDgnUDGSBR/PB++TiQdj9wag5dyyUkPcbWZxRxnAUd8i8zHt+6+PJbBLf6kUfUd/fulPzuTy4qzzcq6OTylEX8zGILH8sycn5OSIGdne3M9gjEpVicpnjZguFlhZbVsQZEOeCu591TlNPU1c0teFnP/mA7V1LU2jeXe0Yxom+63j97gYt4f3NFh8Fl8+XLBfZISKvlY4wxXubu8l72q5FKYU1BlsYpFEUVUXGXAOOrKU6W6kd9TgBUoRhE0gB3CGik8bKgJGCoCJSCT76wSXPTk84O13RHjYZH6DmQJ8EMQqMsXPiLe5nwJnzl1HAR0H/EOZEOT2kkUoxG+ZapFK07cDkJ+bbn5P5lO+Omu9Tmh2IpIKqMRS1JMbvR52C/x97fxJr27qmZ0LPX45iFqvY9anujbhR2OFIO7BBpI2hkSlStNyihYSEhISEQDRSoke26YCEaCG5T8dCSsCtBDoIITItp53hdFQ3Iu65p9zVquaco/pLGv8Yc86197mXE5IzBSiGtO7Zd61ZjvGP//u+93u/9/0rBMG7/Y43t+/p+p5+HBiniYe7e1JKPHv2DKU1bpqYpolxvqHS4geY4f37d1hjWTc1n716xqppuL66oKmreZyhoa4qmqpm3bQYo6ltgTuXhnJJBDN2nvfRypaq79jzKtm3kKcKbnEvLpCpnhlgp03p+voSozPv3ryjO+yoreHJ1cVRm67v9wihqVUx853cwOvX32DNirra8PLTl6w3G66eXLJ7uMNNgcM4IBkJPiFFUZ5ISHb7bp45lDy5umC9WnF9eUEIE84P3O9umJyne7iB4KmN5frZK2pTVO539zcMQ0efErtph1CZi8uai8uWkPLxRhXAJxdrnl5ecLWquVhtaZqa+qrmu2/e8d137/nqdo9PZbRjxn+IKRYlmBiOG5OcKwQ5Q1cpJbxzDONA9BPRO9aNnatwwYuLim1dcV1t6XxiivDbv/UJV8+uePbpc17/4Z/zuutxhwe8jxgULjrGLtInz6evNpATD+/3GJsxxp1vcWc/v+6Ys8Yokcmyic9pdI0WmmkaiONI7HuqywviRSSJhA0R1YHXhibVtGPNIBWxdiT7cZ/r/Chr84cf8eGNeKpbTt9KSsHv//5vUivFt1+9ZZwSh73n2Qtb/Dt95De/eMF2XVElz9tvb1FaM0yh9JmE4t3b12xXcNEKqrrMr1kLf/Kvf8kvvnzLv/jPv+TuvqeqDH/n93/Gb//GK/7tv/tbZN8zDh2Xlxse7ve8vem47xymavmtn13zX/k7v81mVeMGz9vXpa//6otiV5NmNZ8cIkM30B16+v5sSDlDnH3r5ExoKT2+4mdZkId4VH85DUEvQWcWqRdLlXAeyc4D33nfkYJOzL9MOR2rvmXzXUhv5bnnvcQCxSLU8vEhM5vAnleMmfudp20MzfYpkAk54UeP0TVV1bLaNCitCORiH5UzZtbwlBIuL7bFC7Kx1DZgTYuUV1xstxz2jm+/esvbbw/0txNtu2a9tdQbQ91EpEhkN2FrA8YWb8BcjIJzKIFWGUVjFSmXAWctQUuFWbWgMqISJCLDeW+aTDd1pJTp40gMlIH7NlJHg0Dy/fdvuLu9p6kr9CLAn4rTh1Ka6yfPGPqJcSwwvZgx5aZp0FrP/d8I+LldpbBGU1WFZKON4vPPn3F5vcYYw83djrfv7+kPE0M/8XDXl0H/nEHJ43XVWmErQ7uuqBqJ/8BP4NcdPzoIjtNIPwx0Q88wjkfvQK01m80GISXTNDH0PX0/4H0gcIr0TVXT1A2bzZon19esVg1PLi+pKouxhroqg+W1tdS2DHwapRFKHhftyXF4UZ6YM7azIHjMwmdoY7lxThXi+bcSNHWD1sUHzLnAOEyEkDBCFWuo+QZcYIaciklk3xfVmpwy1tRcXjxls3pC1/QcHjrIkhgTwzCQpS6u5d2AUoqL7ZbL62suLi7YtmtScvgw4uIAdEzjWPzAlGW92mBURuaIlgItijRQTqXRrbUu5zBzhIaEEHzyZMOrJ5dcr1uur5/QrldcvNiWirCbeLMfyd7jcoFFyOU7LoP0JUOfM+3ZoJScicGT5OwekTOGTKtORJ1tI9nUipVSKCEJWfD8ouHycsWTqzVfyczBO2QPzKoVPiamDFNKvIyl56mVobaxuJU/Oj4Mgj9clWUBOSaET6QeRFUIFWGQ5EmSoiwZK4lMBD8WRqsOpBAIYyBpSS4ev8e3FFAqubP3PrJEj7DdXBGKBQI9PWjpWT/azoXg6fNrainZ3e1JIdEPE/cPB/puJIbI559cURmFlZLdw6HMMlJ6NMZaUhgRWaFVmUnNMTINA7c3O96/e2DysehWrls+e/WEl8+vuLzYcPt+nIWJRbFqmjzaFA3QFy+ecH11QWUUt8MDwYfiUBDjjEwthIXMMJPLlqTzeB3y6V58RJCZXcaBImO3BKMfvKLig/+eXvuj636GkOZjAEvHQLdUnh8/Lx95ZKVftfimPma3Mj/ETR6jFNYqsih+moKCRBV7MVFUZ8QyplECbLmnKJWa0Rijj2srpRVaJIyaeP+9wfvILibaVTGcbazFqGLS6ymG5kIpcpKIHOfvXaBEJQVWQM6FmS7FnNBKgVTlJ+Q8V1Onw+fii5hEJKlyb6hGoKKEJOn6fnZ4V1RGlbGrkMgRjLasN1dHw97lkgkpsLoqhcWhO+7VUpbZ4qa2rFZVEWG3iu224WLTYmtLJhFzxMyEmf1uIMZ8Wldz+qm0OgbUYvj8q9Caj48fHQS7vqfrOqZ50FVLRbtaUTU1m5S4CmEeDC1KCXmm2x9985qi3FBV1XF8Yb1qaZqGytpykpBHZL9sNvLR+l8YYR+zBWevPOYTc7bgtdacb1YfPI223VJZzeU2MPWJWz8wvErQlkx6vWoZJ8fD7oHNeovWFiUSD/dv8FMJVtvtBZeXF/zk1d+k1ZeIYIipI8SB290diApkQz9ELrYrPv/iN/nbv//7PLm+pt/35aKphJKO+/sbBA7kM0I02HZNv7+lO9zjhh3JTUWCTcoCNQhLU204TwCkFPy7f/d3+OLFU3JOPP3pZ6yvL7i62iBzZPew5/3+wEOv2A2lkZ9SgODmiq8Mky+Eo+whEwlpLHJKUlBJQWMtbW159WRFU2naViFVWQffv9+xMZptU7GVkRUTJuy43+94f7ejw5YeUk7oumEIif2UMAk2jeWiXfFiWyOSPFKp/2pHxo+BcR/ovvyGi01NU2n6riPKTFKCkIsDAi7iZuk914/IoUKrmk3eYJXCCsEjJezMh//4lcepNSmOC/AxgFc2iVefvmJtFG7s+OoXr3n//oE/+/mXpJiwRvHv/Xf+Lcbe8/rbe37xzXsigqcvrvjksyc8eXZBzpG2Flgl8NPAw92en//Rl/zRH3/P29uen/70U54/3fL86YbNZk3VtLx7CIyhQeTA3d3AfjfipsjPfuM5z18+46e/8Tk5RIILZYNbSWIqzvRZCqKAJBRTgLfv98hZV/LDE7DAjmGG3pRkHmCXc3WoSu9QBmLMx77Rh/f5+Ws/mts7p9Tn098LEvSYebqYXZ//fPjaKZ4gusR5pXh8c/y+K1ZcOrPerjDGUq22R/H4fX+PrS3tqmG9WqOVQEpmiDcX9qVUCKloVopmVXFxteGu7ahbyc2Npesjk8sEZYnZQqhw0+wEA0gR5wRrJsspzcXlGshMUw+p7KGVXeN8YHKewY/kLMq8pRRYeWalBGRVlHhMK0rSnQVirRDOkJzg5s2uJOAUdEjk4q6TXVHIun7yKd6VypDZRF1pxWazJYTA7d3dLKZe5CabpuLiouXJ9QZrVOllCsE0jCgladuKV8017dpiK8nd7Z4pMhNictGjlYKmtjSVpbYaKzSajxOdX3X8eHboHGhqWz3KeAtef8qMpRDIOVNWSpbFrcpM0DlZoXzBFmtsmfURpVJjqeAosGbJnmeYgqXPN8/ELYQHygnhbJ/6cJEvPfBHNw/QNi1tU9FtBvb3ByYfuLnbsYktm03DalWTskRLT3cY8FNgs15jNFgrOOzvyKkovlxfPmXVrFivGt7ffM39wzv23UPRwgsB7xJ+mhi7jjfffUu/2+Fdom0r6sYwOV+MJWNCaUUSiW64YxjvcH6HNmXQlSmQRBGrlcpAlkem2/Jdd/d77rQmEqmuWhKOh5vXTN2Oq3XFk00LGYbJ4+dqcIEuhOBoxutDILuyeckUsbXFKs2LtqHVkkYJZBbkAH7KPLuqqSrBxdaiUsLkTMoBPw2Mu5LkKG2ISTJlcD4xTh1GCtZKkX1iyJ7Jd1QaUv5w9OPDEPJDR7nGxlh0pUiNxedIHB3jsGeKnjE6pjQWZlmMhJSKBurkMLrCmob6wqKyJYklW1+C2GMdGET+KC5+/AnzI2z0o/ApJNIo6qZitaro+4H3Nw988vlzXn7yhCgNnXPc7Se6odjNxFik7JRSWC1RogT+r797y83Njl98e+D6xUuef2YwVhbBCK349qt3SP3A6uLAJ5++oq7XTMPA1fNXNNtrhBE0bV0kEAdfKuSYWa9XGKMZpyKFpZTg/c2O/tBD8oU2H+pH33kJQqfqWMwjFQkpS19ILn1BpUtwmMcmliD2YSX3Q8HrvNpcHvvhz2MWKMfnL6+xBMqFugSP3+vsYmFMhVASHx3Bh0JesxmZBSTJ0AXIkspEgh6PwuWSOR9SEEZPvxsKdDq3H+rGcEHDqy+23O9Guj5ANYAtoxgi1pA1EUGYPClOWKGxShekQClSiow+YbRFSoWfiqj/5H0RVZeF5OZTwMdz5jOQMnVlaW0DJhMJDNGQRohTJuWmwMsJcpz31ZhJExhZAeV7GKNK4GoUSUZcKKQsH1zp78pyzUOM7PcdMYTi0KMlttIYo6ibDjRknRnGnnEa0VrhRElPELM1lpZsmoZ1VVOjkZMgTx/dgL/y+NFBUEqJUQZhxKNsalGEUEfB6QLJKCWOpJWjC/uMB6fZ6LWqa7QsUmsyn6jn5/NCy49S+mxBw4fr8txi43HT/PT3H8rb66qiaRradkW3H5mC437fIYzC1Jatsdgssbp4HKZQ+mhCFHuTaeqOOnpPrp5zsb1gtapIecT5gZTATY5hjOSkmKaJ/nDg3ds3dLsdORs22xUrXzOMDucDMcXSlxCJYTwwuY4QR1ojUSEX+EVSFpIqycO5+hE5c/9woJKKkAP1ZcPkB3b7A30XaaxkU1uG0aNmVmhJPPJy64MQR4k3EROKovtXS2it4uVFS2s0lZbc3fZFl1RnWmvZrDSpNsRpIrsyZJ5iILkJazVVbVEBRFBkH+mniY1RtFqQfGKMME2e65WeBak/OI4C6B9c0fM1kUGrwj6OlSW4jujHo3pNN/UMcSBShvoXYQDvPFYX2C8mT8q6nJVHbLPH75s/+NcCSy2/Oh+R+BXIHjGXrbeIOhfd22FyNJuGZ588IWTF6BL7wdGNrowRzGiLEGC1KpqMIfPu9QNvb/bcPDj+5udf8PTZJVY6XMgMY2C36+f2eUJ9XkaapiDYPllxKWAae7Iosl7eFclCiaRuKura4v0eoQRCy2I75h1aMsN7J/g6U3ztlspumRPMgmJRNsP6yyyg1hKpTsP0C6X+PIguJ++HwIElAKazm2EJiCk9hmlPAvMnZGlhix75BWL5edzDRYDUiyh0qV6TTIicIAlylPixWB9FHwh+9kz0BUFTUhBFOlYzUmiMVShdzkFVKy6fNGAFuivkJmUTygpkLEmvErMmcCi6xkoWp4ji5JMIPmKVRGSFm8Zjgp2EImcFssz2hg9YlCIXhZe2bqCGKCIqZILORFMY5zHm4kAR5nOWIPagsSyonDaykFQaySLC730gplBmdeeRjRgjQ/B4H44olNYzC70a0Y1E15KQBpwrLiFyhtOlzEgt0Uazrmvaea/GUX5+5PGjg+DT6ye8evlyVmVYVljJmBZduGVxphyPwbEsOAob0iisPTWly01RHieFRqhCjz5nbh2zs/l357Mm50FSHoc+H/cCTo89ve5ZvchmZdlua4ZhxeAddAM33YFsFbKyvFQV69Wajb3i5vYdw9Tz+vU3hekUHF98saZZSdYby2pdVBFiGslZMo6Bh/uOh13HrhshCQ6HA1oK3r9/V2Bg22Aqg6kUo7snZ1+sY2KBQu7v+uLALRqMrvFqIMtEexFQViKMR0p/nG2aLwv/rz+/oTIHHnYPfPZn37FuDT5mLpqWdVO80ipraWtN1y/ivmXOahmXKHqHiWdNzbayfHKxYrWqWa9r/sbf/IysFC4KHv4f/wqZI0/WK9ariroSjN0OnYpKz+rqgs3FiqunLf9V23I4jOzePcDgiWPk/d1AcpE8Bd4NDpczgcy2bj/A9gUiLwPSS7b+Axj3/HstLUhNAg6HPWP/wLpuUdqiU0TlYr+C0kc91NTM3R2pENIeb5E8w5mCAEjyD8Etj6q800DE8rsf7neVtf3+7VvyZoWb4O5uTz9M/M3f/21+43d+wovPn3P73Xv2vcPFyDff3qCN5ovffMU0OrpDj8hNoa5fNvj8NcomvviNLb/3Nz/n6nrNz3/+i6IdieTVpy9ZNRWvXl7z5S++BW34u3//v4adpa3+8J//S4KfEDLys599gbWGsZuYnGO37xAKbGWo6prf+e0V3aHn9TevedhN3Hf9o+8WZpPl8/68EhLkecXn5/s8z1VEEee31tK27SNoM80iGh9Xe6XXeh7QYpx9P93JfWLZL9br9bFV86hafJQ8z681z6KdH1EYtLYY1SBnlbZhTHRDZHQRJStySIz7DkWZGR47T1sV2THvJkKKM2tZUFWWuqk4dLvioCEFT68u+PRVzRTcPEYiCKNHCkGzumLcN/jBM+xGnBCEEHnz5oYQEt5H6lEggf3+fjHGZLV+iqkrqrbC9WkmCp7dPQvgJsEYXcYjdFNugzazWa+Kg4wPCDEzQBH4QyQ7iRAObT31KlM/XYNQZDGy7/YEF2hWEqUk3kn8NJW9Gom2K2LOPBwOx3YWAi4uV1w92aCVJEdB9GkeoZCsWkNdW9q64sXlllVlEUPhL+Qh8GOPHx0Eq6rodapzSEGUQVWx/P953SwElDxnU8cb/7wnMmdhx8pjfu7SQ1gW/eOK8yx4nmVyH8IVCxtseX6BafOjm+T4kVJAEKkqTdNWxXH8MBJyZvKeafIoq6h1zeXFJY2vudsHxrHn/c1bmqYheI+WGqNapNQ4NxUSQUyQBSHB6Ip7xBgyyt5wsVlRVxZrDqUnqEVx9xYRISM+KJyHsXMoGdAyc5jHU0KMXD9ZYVuFNcVEdQqPKyarJbVRjJXFhUg/ZbS1RY1nZuIJkbCm9CoQZdoIkZEzvKwkVKoQW66amufbNW2jaVpLbTNBQJSCZ0+3KJm5frlm87TBGIGLEZMiJmfGPqJtYOUzbVsjpSL0A1IJsvYMvcJlcCkjYkADa6u52Fhc5KMKTxxJQBnxUVg5raUS2AppQM69A6k1YhZlFkIhyUXzUhRbGCOLkkkZYSmIx/mKyTMsegxv+ew9j+v/FPiWOvAMTeVMmev4mofdnlZJkAqhLaZq2LQrtFS4OdD1/cg4uSNM/+zpBZUtfpeji1SNQEjDp198wtU4EUm0raXowM7wcG2pf9oUWE4KfMzk5HFjXxAZbahXLeMAfhpwo0MKSdXWZCHwwjMOA9PkiCGjTBkYv7rcIJSmc9PjKu3DFsTxj+X+XSq3EIr5cwlU4L1/9JxlsH6pGpeAuAS3I7P01Ho9klGMKepG4Wwc4ENY9ewPZx/8h5KscpT3k7OTQnmciwmXEn4eLc1zVSZkVVAWVdaakAqpUjEKF+AnB7EQzcJUZi6lLgwJoyRZSHLK5OSQpiQTITuUkUgqJAbnAs55AoKAIAlJYFb1EiW5EEqSCPgg6LtUHIDS4+9f/CclWeYi7D+3mchLi0sgUAgBxhSTgKqydGrED4k8RdqNQrcNVMU2apwycoxoC+vVinGYcMNELyIxQcqSpi4IQoxFt7mcN8mqrblYt1ibmCpP9hJJmQlfrzRWa6w2XG1amsrQ2gL9D/q/gCBY2zK+UJwFlszpREXO6SRMK+ad6iO8fYaGFguTMtR+UtoQiGMAXH4W6HU5zoPfckMsGeWpL5CPg5nLc5gbwI+DYCbFESg9udWmIUvJYQrEnBmcpx8GFIqmKSzLmD0uFY3T27vb4irf9+Um80UaTIh4nB2SSpNQjFFwf5gQvWNwgSdXl7S1xaiIkhkpEkqnUuKrhPMUirJTM3s1M+UixB1z4uWrLe3G4KcOIz1+PDlECwFXa8u6KQEp5YlEpl5b9Extj8kjCFRmrqKFIB1Zj+VKGiXYWsXnT9ZcrVqu1i11BVUl0SIQS4nP558/wVaS7fOa5rIlI+kmiw0OGxy3DxMJSb2pWa9XaG3oVxajQWjY3Rc/xSlppJ+wCl5eWJ5eVXThMfS19J5/qKJafpvnwFTUOSSSMNOvDdoYRAikBEJoEKCkObL6rG3ntVlErZfB4tN7z/2n8/fNnL3vUoeeIt15v/CUKz6Ogrv7B1bWUDdbTLOiQXG53SBzprs/sH840O07xn7ixfMLnj694IvPnhFTSbaGKdD4TBKG3/69nxGDZxw6lMpz5SSoVw3tdsvleku37/n+m7dkUc59t7tHAFXVsL28QAiBGyf2+56c4cmLpyAKYWq/OxBHT0ojdVtjtOb6ySW2qZii/7hXMR9Snu73JTldUJsY07EX7Wf6/NJPXCq2qqqO93yag+AwjsVWiUScLdCOLFBRBrWNMeTMcUbtXIlmgUPPk+zTHpGPwMKjFZcLI5LE7BKjyGRc9Ewx4qKYvffK62hd3EG0VUhTIaTCzALURkru7t4ThCPHEgARc/xNZcEYBYHST1PGkBGM3lHrFmtKO2e/7+jcA0lpyLPJtQSRM8LImaCiidkTJ8/oEsrooviyrGMBpjIoK8kqFd/BOO/lsYy6FX6GQMlSNNR1zeXFBTfc0+uJPnk2G4upNEE7+tERb0dMlZFZ8vzpln7fM3Sq+P5FiEmwWVsWJR3vS2JjtObp9YbnTy6p69IiscrSNvVskiwRUSCS4KreYJWm1gZba8b+v4hh+dn+Jx/hSYhzLymTC84Ls4rM7BvmT2oEOeVjI1MrUwLe7L68VIZx9qk5VxU4D6RHK5Szn/OqcVmx52yu47zbjFnIc2o2UK8t7baCAcbJE1zECCAFvBv59vU3WGWoZcP11SV1ZamqhqtLzXpdhH/fvXtLd+hYt9+XYf+rC/aHO0IYqeuKq0sQpmazXuO9I4wDh4c7+l2m0oHKKiorETJjjKRtDetKolZgdKBuSnbTH3q8i0wuMu7egDeFDWUs9jxQCMHf+RvPeXF5Sdc5bvcDY4yYlcL3Dt97LBKXBYTEqq6oK8t20zAO7oi9X7WWlxctVS0JOO4OgeftCt0aks5sL7e8vHhGfrFHmURzkdi8+BTTrPni9yTT/R3j/R3Dn3xJjBPvv31HXO+xRnHZ1MhVQ/CJ/usHPAmtMxeVRhtJta3pk+BuTI8DDgXiOWEKJ2ibs3+VIF6hrEbUgpw2WKNR0mBMLNBfNkDpTxRZPV0C4/HVi7nuHDHn3+lThZd/OByfiGJn98/8P8tnO8/FMpl+3zNtRlbrDZ//5FMOh4E/+Zd/xuX1itW2JoaSiL58csHv/q2f8uTpJZ9+/oKvv37Dw+2eIU54J8hBcnm1RmtB3bTcvr/n0A1IaVm1G66vrmnqmpQkZjXwX/9v/L0iOC9Kjy7EjJ8GjIIn11u0EgQfuHt7w3CYmCbPvusLpNY0TAECGWkE7WbFxTidIT8QgiP4iZwzYdb6lIhSeSPmOb40I3Dz2YwZP3mCCDgxsZxlbQpj2WiNrSxKKerKknTxxez7oWhTTu4o3p7OTvRS/QohIc3zicE9XkLLhVqqOSHwzuGm4dH1DD7iJk+KIEyBFIvOZSALj1QJpTNagRuLsEg9u18IIckoQkxE54sHZCzG3dYWOzkZod8dGPoDm4sGqQV1ZYmkea7SMfiMS45KePAjtXBstw0gCL7MDfoQGCfF5AJxmGjqqpwDIIdMmB4jSElFogzzDKiYG7tALCW2NnPIyJnaGmo7j3pYgzYBaSY2lzUX1xVBj4QIz18Jhr0nh0ylNP5yRXQR8dNXRJ8ILlBXdUFe5HxvCEFT15hKY2uNMqXa/+LTZ2wvWurWsN4KfB/wQ8KGGoPBigpEZnBL8+GHK/nz468QBIt57tLnO5b7nPfZzjaisztdCAqJQ5TGd4GZmO2F5pz5+LofN7GXxwhxyiKPWXn6sFf4uNo7HyDnA/hDCIG2pYkrx8VdLhdqsAQl89H4cggjiFwsmjb1DLcpoPQoHtwDbohUVU2IA/vDA+PUI2WmNqoMMStBcJpJFccFcsRaSV0prC1SP8ZKaqtpaokxkqoWtK3FWIUInmluv0qfELJ4A8aYCOPjc9Y0ls2mwmrJEAJpLIte5MLYNarMHQoyjTUoLfnk6QV9NzCOE1IKtm3F5eWKSheF/CoXRYZ6VZT/p2EC1TPtOoxNCC1YOY+sI+uVQccamdesW40bPcmPhD4hjEbXFVHrYojcmEK0SJlEBCUJSuJT5uDjD6zjY833aM0thJlFqqzMRs3V4OxEII5kh6LYk2emmZIGpTQxLVD/vHwfQZcLvJ85X/L5HIM7fpbH/zy/JU+V4ukBQ+9wo4ccaNo1KQmcT3TdRMyRw75HxIjVgtoojJL4yRVdI62oZmGDEGbR+FSks3zMxFQYuUoKREpFkH1yRFfUY+rZAiuSyCJCLmasSpaAU6whM97HOUEq5BVjzDGRHadI8Imhnx6dhBiLKknK6WhhVoC+eGpzHPeQeXEvqfEMMy6nyofZrUFpvHfoWWYRSuIRgyf4RaB5RqYeZVDi2PM79hE5tXTE2fud/p5nO7KPBepL39wfZSCLMwYgiwmuESBlLB1kwczKhIXZuDTfjLUQwM/zmgKJlqIQSXzA1QabNVrpMncoBEorhAdyxE8D0RcR7UU3OouMCxHvikdkinOvLKWS2Ilc5AM/XLey/GRR0D2RBTLJ0/PljPAtBQmieEPOdnEpRaQCW5UZRpWLaUJt6mLhFhK5shAyVmpSiMQpFHssWcYpmK9F0zTl86gEMlKE+RWbi5qqUay2CWcFvk6Y0SKjRsbSYtP6ca/z1x0/OghGHx9L7HDWLBan35UsqpywE4tHInXZhIwqc3sFRi1MyAJ9LotsDlpz1ZhTKq4TGVAUrPwIsTKL6M6UXQrJ5jhUL09zh+WVBR/OnNnGYGpJehhJ3pG9o9FFFd3UBmsl0+i47/Ycho5KW55dXWGtRRtN2zZFNPZwYB93CAHfv4YQp5IV5sDKSC6tRm805IroLTkWzT/dULT9tGGaAlJljIWmUlitaOqKdlWUc/KgGHHY5FnnGjkJfBeZ8Ow794jAGFBEpZGrRLgNDNNAd+tYrVc07RojJnwMaAVPty0Xq4a/8zuviszdOJT9qKoQbY1UkUbCSwNXzy7Q1vAXf/6W198d2Hdf8f3bWzaN4nd+smW4H7h6uuXFq0skLbVt+PRZzbhzdPcdrncMWYCu8MYQleL61SUyB2R2/PIbwRDgPgmmceJmGDgP74tMen4UVj6syMrvZVaIpIhBEGKe5ZZicZ/OoEwDAlIa0bPguAs9y0YYQ0IFiVrYqPPbiVzmo2BJBM9ugg833rPjg5B9+n2Gm5uBy1XPyxcjzfaSnBWm3fKwv+fN2477+z0XreaT64a7N+8Y9wce3t+z2qx4+eQC2bQs83K7fV+YvTPhWjUVK6PIMbC/vWM4jBz2A7v3O1QYadctV0+v0JXGaoVS+ZgIrLebAvU7x/7giXg2lxtsVZUfYBw8tzcdu7s7Xr99f1RXyYAfR9w4fMwZ4JSQ5rPA8+g4nnax5B/AiT0uhZgF3fVRzWixZXqUSH8QCI+KMefvd0SXOP5+6enFGItU4dnLaKWQIhPjiFjYjMpSVYIsi8ZnCo7kAo2RR4sgn8JcnZbETCtFvbJ4N5U1KQxaappKEzqHGzwdE3klqHSD0gKtoLY1cYzEMXL/7oEcC9zoh5KEeB95uN9z6IvjfG0tTVVh8omMqCli7ufnW1qJmMmLaVqCoCL6TAwleS/SiZoci3bsOHq6bmTfDUzTSEgVqEwWEakUWiguNytkFrhuwmSJFopG2bKJxzTzTU6fRghBVdf46AuzNDlSFsSki7mvjiTRoxpQNdixJQyJ6WFES0XmB1jlv+L4K41IqEWifbmT58i/OBHIWQx38fVKuQicprlHl2ImhVNGWBqgc8BS6sjQW44jVk8uf6NUpHHJGhHHExeYGWjHDXL+3Mtr5lkWLHiOEG3O7HcPGBXpdgM5lcrs6fUWYzXaakL0hdasNEoUY15ksa5JQ89hf49IEZFD8YETApkTtckgIQgJIRfx7JjnitqRcxG9HVwiuUCkCNdmEoiIkYpKW55ftyi5Qcs1m9UK0oGhu6OpNlTWoK2mGwYmv3t07ob9yJ3cc3fY8+62px88OQYeDgd2w8DV2mCN4GpTYVXFuq2wRpCMQiWLNoZoDaGqeUgjSQv0tmI/gd+NfP3NLV2fGEc4HAqb7Q5BUyd8ExhloBp7bA+pG9AJNus1dykQI8i6Oq4DQ6Q2mdZKhucN+ylxPyV8kIj8AQtzzrPOCRYfRp3jXHsqQdPYFqaOEDPG6EKEURapKoTIJfGoGpQyTN4hRJlxLQhDhiRO7gQ5U2yUHr//h0E5P1qFH338x38TpXJv1zXVqmHoOkJU/K2/9/t8/Rc/57uvfsnXX7+m39ZszBNkfl8IWFnyt//u3+DJs6dUTUM3jDwcet7e7PEhIWSm3TZYq1Ex4l0HzhFiIoVEawUPb9+xv9WI4Ak541NivztQtS3bq8uZABILY7OtyKIMJm+2K7bbFcMUeefuefv9W0IYi6fmfGil+Ht/8Ac8e/7seJ+ff+cPk4JjNbicVjKncRg+Cpow9xkXeb98QoVOj3mMBP3wFTl/z/l1j4EwkRP84stfIIU4JmTBTUVgX3iS0eQkkTKhtUIaiTECU6+weosBcgLnM6ayMLdn/BjxLhACxARIzeQ8noDVFUpoKtvSdQPTFBgHj2lUaRfUpvASs8DUDW6KTFOi6z0xRMZhoh893mcUispUtHVDbcAHRzcOpCwJPjxajMooTFXs8IZpLL1PL0mhjH5MPiByaWkFV3RGYxi5vd2ze+iQ2TOONW6qmaQHkZBixCqLkaYUF1mis8ToMn+bYi5+Mzlz7hYy+jD3fksbQiRBjoppCIgQ8HLCNhJTKciJ5MBlT4wKl378jMSPDoK3t7e82WweLZaYAiHGWWtyng9Res6SBEkUX7m4aHhmkHkmLLCQZeYgqPVxMbP0+mKch+Dzo9nEGRsFIWfT3NkhYcnujhc1zwP3cxCMiZu720c6gDfvH/CT43DvCL4ol1sjETM1f3TTPDNWAlSMZSDZOYd3DoJDy4xVENsKpQQyRZSRCCUIMpFcIk0JQtFS9dHDnCB4VRhUIaZZnSwRc0BlSaUrcrjAR8dmCjiv2XeS3SHT1hAaQYWmG+Aw5BPpImfevb9n7Cfe3j+w6wPeR3QqGoelsl4VAe3gIMEwZW53e3w/EZzHVFXpe/jE+zBQa8G1LGLXQ+f56vtbxinhHIxOMMlAvTOEO8tKOWoXWHXQdBnx+rZAzUpz8+BwCaQPzMIcmDTRmIyvM90EhynxsC89j/1hfJSZ7+97lBaPd8/5Wp//qyAHO5SvCHvJw66nH3sqaxidox8nlC8OIggHeUKqRNcN8zqWRO/RSVBVch4dyXNgXYLguaj2cv4L1b9swo8rjfPPub8fTmEyQ+8cD/3IzUPP4TCRsqF9+ikuZw6T4939jpQ815cNh76MxBymxNPPXlJfbKinwMO+5/3djm/f3BdoTWa211vq2mBTIg8DaRhIcr6Js+Lm5oGcMy5FJl9YxmNIrC8CSVmiL5CrtYbdoWeaAi54kgCUoB88t/c7Xr95j5CRXd89Cv9KgELMsPPpHCQBy1zqoysoHo++nL/WUhGyJLqZmU7/Yd/4DG08+90PluE//EuWyZyMAMWjUZ2cix5oCBJEKHN+SmK0w2SDTpqQBMlWKGlmSLhUTNrO8mEyHb1MR1cS9HEqkHiZ9SvSdDFlHnZjIa0cRqrWlkHy1qBF6VmPU2aaIuMQmEZP8Iv8Y2GB5zgzV2MGn3A+cBgmbJJ07gzdyzB2vpxpKej3jjQlpIukJIqwdnB4l4lBMA2h7OXKc3dzYL/rsBoe7geqRuPlSEYiZSD2EqsMKkt0FigkTqUjMWrp3QpOZMeFg5JznBEbgQuSLD0oj+72VOviGqEGhTtkunuPzIK7XQePVsWvPsTHEmRnfxRHuf7CzPoVrK+PYJ4PH/bjPsv5G3/cY/n4QT/ifc76DGcBYpzOslUzw2vn+9Wj/8m//qOcUQI/Ikk82vc+fpH80T/K/zmdz8ckoOXzn5QzymOWnor3JwjAztX4SbPxcZCQ51n1sqVL8YMb9/IbedaiSfHUGy6VWXn+wvw9a+8cX3NBmn7odJaeTGZhup8HdH82/qHtY7bwrzvOR2/ORRbOhRWWB5yf3+W7H9/lxysw/egj50xwp+9ltDr2lY4bv5DkmB6NC8lHmzGzw/rcm1kqoV/VE5+fdH7+zwlkx8AyP++j85zP1ubx76VaPq2HktAtR9M0P/p6/X/zkVI6WsUBx/Gu8z3m+C3n+/JsC/mVx3nx+6s2mnOS4PEtzt7sfA8/EVsfX2fx6Pn59L6ZGVkrhzLy13+c+SZ6vEdy5GYgOJoanBXiZ4nj4890fNlf8fsP//b4BfLZOV7uX46f53zfyDn/ykX4o4PgXx9/ffz18dfHXx9/ffz/4vHrguCPhkOXEvVDPT7gyLhbcs7HqiwcK5YfOj7+23kdtDzo7P+dve+vwjh+KLCfZ1HnHmLL7NF/+ceicnFydj9Lszmdv1+dRZ8n2DkXr8LlsMb8f8jAzzLFY/Zf3k+IObM7+zg5Pz6vx3OWZ4LUWWZ59rIfvOUP98l+9Xcrn/8jz7e5Avlglfzo4yPW4H8JxzEzznxcdfLD63ABBP9NfNb8Q//vV73uD62bs3v+/H4/r5yZ/793p36Mtfb/LyrBnDPu7HstvIflb2ePBB6jQo/2yzPEqJDZz8Dfs/vjbDd9XHGdtQaWR54fj27BpTo9QzU+vBYfIS1GHd/9/D45oQTnaMHxRWcI+hxdOvt0M4v09N3/quvh43L6yCg+3lTnjy79/HNxhF93/KggKITgH/2jf8TFxQWHw4Fvvv2W9ze3fP3NdzRtw3q95urqCVVV0zYb3r1/wzB2pORpa0NTGZpmjdGGShvy1BdTRxLb7Ya6acpmOsMqkjLY2TYNxhi00azaFeMw0PU9796/LyW8KuzMQm2PZIor97t3N/hQnIxXbUtlNU1ds16tiCnyf/gP/884VyjW/+Sf/BN+93d/90dejH9zR84HvO/5/vXX5FRBrrm7dWhpadsVzjsyZTZsvgrYShNjwrtA3RaH92bVknzm9evv+e/99/+7dF2H1pr/9f/yP+DTT15AcjMlXcy6hsVQcwpDmTkSmdtdT0hQN1suthuqyvL9t98TU0Yg0dowDCPffv0NOSQqY/iDP/gDRjexOxz4oz/5Y5ybygxRjpATIaTS8E6J4BLOOYZhZBjKcPMjcQUpjnBG2zZoI9G1YrVZ47znn/7T/+i4oC9fPC9i6jlijZwVQcp5iTFR1zU5F5H1RYWk/K4QJqbR40M4U/QpN2aBcIobe0zlsyNAiDJoPGcB+BAWbOn439K/OLGSi+Zq0Ve0Rs2zVJAp5+Hp86copfnTf/0X5JRpmoZ/+k//KZ999tlpfQBhlq5LORHPBr3T8b+LUkr5nZg3hDxbmCUycs5Hg5hJaswJzuzWUJzpU6HwazVb0th5trYEuxgTQz/w1VdfsdvteP7yJU3bUtc197cPQKaqytjQd99+y//if/7v472naRr+8T/+xzx//vwRW1OImcXNaUMvgtrLBnzS85RSzubN4dGw/Ok+OgWYc4nF878vx5JkLEo1y78fP39OAnP5r5IZJSO//OUv+R//T/794+f4n/6P/odsNxdIYTnsdzg3MQ09WmSUgGalj24Jb97ccX/7wDe//Iqm1tSVZnvRcHmxZnuxxhrJOE3c3O/pvAAUl6sVKxloRKAxRZlHVzXv3t+z23d8+/17dFWjjUVZW6QVcyEKppyYYkDXxZro8mrNZtWwahsuN03x77MGkuKX373nP/jf/u9JKVE3lv/B/+wfsd1eo+WG/f4dzo24caLri4Xeu9v3uFA0oKtWYusiMnJ9saWuyoxeCI4QpyIIbho27SXr5gm1XbNdP6OuthjdIDOEWCTeFrKZEsfIXQhssow5ZQq3xAWPj47JDXz//ZeMbo/3PVVb9KuNMDy7+pw339/yv/lf/e9+sCD68PjRleDl5SVXV1dIKbnsOlLOdP2AEOWDpxRRSrG5vKRqa3xw7A8PaCJaZi42W6w2VNoSR0XOgSzzMQiKLPAu4CaPFIVhul4XY0WtDavVirqujz0GFyMhlw1EKVVklkQRkh6HoWy0OVHXFdYYmrpiMztin2uPfvGTn/Cz3/qtolKwbGRnzMMzFP08tzs7M6cq7ix3O+vrnD30TIYppwM+9BirSdESo0XrHpIsGqEhICgVwvJCttLEUBrb7bowrZqmJbjAbjce30wIeP70ms9ePYNcGsUCiUiSHAPpLAgGkZD6nmEKICSXFxsuLrYYpQrZKBW6dWd6DtstRirapuGzzz7h5u6O0Y20bYOxZaYsRVd6WPNzc4KgM9aYozBymBUhlCqbBECIkeADbVsXMWEraNsK5c6qozI3U+aPhEDPQVBpNZ/zRSVGzL2yQnCqastisBxCmrNIKEZ25SqfFEQEIkaimAfk5z7dUgmpOZvPQhx7HkKc8lshyixeimVTVboMEq/OxMCl0pzopuU5P/3pT/mN3/hNlh6bT4Hd0BN9cXHwwZ/6fXNVXkyrlznbD4Lg/B1lKtJZQWRiXv4iihpJSLiZUCYks9/did0thCDPAX6aJhKJruu5fvqEpm6oq5rNZksGjDZFSDmcFGOklHz++ee8evXqbI43Hz8jZ0GwqLgU0+vzILhk9N576rpGKXUMguePWVSiPkSozv99Xml/GCjPg2i5qMWv1BqJxBWXlaW/JQTbzZqLzRqyRuWAmyRD9qTgEDnR6GUNKh4k1DJTy8y21jSN5aKt2NSWtTVUVqJSopcCtAKh2VaKjYaVElyuFFVdU7UrGpF4qBTCjei6LsGxNhipioWQNvgQ2HU9stbY2vDy1TO265b1qmG7qjFaUVlNDMUn9rjbScHqQrO5tFjdIExD8JIULHUn6QfNkDsmF3AuYJpi4Nxua66eblm1NYiE8yMuGLSStNWGq9VLtu1LKr1GqRUkDVnTtGWwv8jGlhlKSSEJ5ZSQqjgMKa1xfipC6n5CY7HREvNLhqlm9DtsXWzgRFJcPlszjOMPFZA/ePzoIGjmTSylxOUc9StjuL254eb2lnHoiSnx6ic/5Xf/1u9RVRV/8Zd/ycPtW4bDPetVi9UaIxQTFTlrTFVOojEzZTh4xnFECnmUO4oxI3Ui5OINaJsVV0814zSyO+yPoxje+yM8YyRlI1VlkJZUZMycc3MD/+wGACYfeXjomaah+MshKfp4y0hIuWkF+TRc+qijTZmnmRkUJYsvGw7yrCk8v3UmFSmz7PGhwXtwLnB339F3nm7vj5Jf+jg8yvH8h5BoLyqUVihlcP3Im9ffnm7iDG7scUOHkqnMx2WBzKoYoqZAztP8ORL97o67+wNvb3bk6NHyczbrFX5mmb1/+579fs84DjQXFzSrBpTg/d17/uhP/5hD35NJKCnx41CMd2Mu5xAFQpYNWUqUKqdOJKhrizFl8L7rHON0YJ0qYlTEKSL6zOhOIy3MG6IQFIbofL29L+zXGDLkqawTY1GKk0LILPcXF8axOCUqSxBcNleZSoBZjtN5LRqMad7Il4B0XAOiBOGcBUImhEhlI7WKi8v1LAOWGUdHCOkHb9CcYAqBbuz5xbdfEbqBME74VNwuoihsy+Pa+wBZkmfJl0CAD7PiTaksY05Ia1AZVBRkJSk2BB9XVSmdnQUhMbXhqrksBCY34r3HWk3MGRccMkD0j4fKzxNOeT4s/sHfAayp5nWvjp/hXEPUWvuDgezRaMT8u/Nq8Vx79MPnffj843lUukhztRXBD1hbnU52zhz2O3IIhCmS4khwI4f7W/b397hx4PrJFmvL+n7/7WsO+wNpvEOtBRYJ3jPsD/hhxCrJMIzc39yRpEVqzRCKAIWxYNoNK23YtpK8NbSqRqctymqkkdBEWqtZVy3GNPTDxNffH8g6Y2vFZy+vWbfFMk5BkWYUEHGkdGqh5Jy4796Sjae2A6M7IEg0TRG1yEpid4YoCss8xIRwCT9FlNRYWyEIZBFIKmCVpLINjX3Gyn4C0fD6m/e8fXfDft/xxRefUVU1tq4pxUMGIn3X4b1Hq+I/W1UV97t7JjfSjQN1U2OtYbNeoxlRsiMKzxQCfoy0/TsO092PbiP8eFPdGWYbx5Gh7wne8fz6kqeXW+JPv+D7N28IOfPLv/gjnl9fUD15ytOLC1R07GXm+moLITJ1Pbd3O2IKXF6siAGU8rip+HIFXyDMZfh1GEcSAjmWwKC0YrvdYhCYaaRpGrTWbDfbsrmnyKsXz46KFq/fvOHQ9Tzs9jOsEk/DvDnz85+/p+u+4+svH/ChDNlqpWeTT41URZlFyHz0STwKPlDK+WKQlvE378khoI2FuiUZyz7EUgAKAVGQkiBFQYyemAI+TIQAIcI0zmMSqSLGAsnkXNQaCiw2FlQuZZJ2ZCEQKHLoOezuiPG0AVpVtAnFHJwEApUVi+aYToEkJEnA08stMsPt+zvG7sDD/R3Pn78s4rshcH97y/5wIHjPar1ic7Hhy6++5ObuBkTGVJppmrh7uKO7v8c7h5KGVbumbVc0dTuztTzrTYtSku12czQ8vrm5ARHJFIm4qjbU64q6remH6TErjiKKbCtTjEpFuXmVlFAJlJ51SH1fFGMQ+FAgQyFKMidCUUWJolRmxQasnKNFNFjMVedj9aFSeeYZWuSs8lg255TiUdxZSU3OgmlK3N125AwhyOJc78Pje3SGLctXLRuy60feffk1u7fvefrFp9hVS71pUfP6E0p+NFsrBCBLcBYx8+bt14z7ju1qRXATwTva6wvC5Nnf7dm8eEa1WVNdXMxuCY97/iHGI2MXKNWmDxxubtm/u2H72XOENXgEvnM83N1xTit8fP5mRvOcFC0qUctcmK0sWhV3hnPt3+Uo64UjNL5cp/NK8Nxd4jz4KaWo6/p4rqZpOgb68+pxOUpuUKC5Il79oeVQQqSATA58hwwDhgH8gTAcGB88drth3VzybKNZK8ta1Ty5rmmauribJEEORcFHxsCTVmOaCmM127VmU2fWVvBkq2kaQaMjzkZUSKRWkGQAmbGN4MlVxfPrZ9TNlmEKbLdNEaRXmifbFmsMRkuC9yRf9Ifv7x64u3vgPFqYRpGlY9+/PypKDa7H+SLB5lw8oilNXVNVmro2CBkRwtO0CpMrmiyJThKi4t1txx/90T9n99Dz5rvXDFNBED/9ySvaixVPnjwvLREBQiS8c3jv6fYdzjn68UASHgwYpRncwGE8cHuXMLXD2EyUvrRAciSJkSx+vKHgjw6Cfd9j56zdO0eKgeZiS2UtWpee3L47cLvb0e1uqU2RsDFKUVc1lTG4GHFu9pXKmZSLNJTwmWmcTv0ZefITc74ojcskigRRUKw3p8+1wDdN05CCJ6fIui29RGst+/0ON/dTFlhlOXKG1687YjzwzdcDMZaLq1VC64RWCWVOotZLIiuW+yGL0iNKCRE94/c3iBhYb9boC02uFfeDw4dUZgG9nAOgJAR/9FcMMRPi3H8SqswWhSJ3FQPEKEgx45fBOgTT7C6dc0bmwNgXUejTeRFFJovi1CyRBRYVpf+WpSwzbhJWTY0bHZVRpFBcAlIsCkFumhjHoQQ2VeTdtJZ8891rDocDmYxUxRonhHBSgFfi5BOnNEkkYgoobTBGs163CFGucTHQtAixomkrqtqyWtfYunpE94e5sjvqwS7zXCWACSGQSs7XubyXECUwLQovxhjEvAkfwW5xqhbS+casltddAmXxbwwxElI89pbSrHhPLrOzS/9wOdcpwjSWjbuo5i9zsz+Qqoq5Mpn7426cGLoB4mIzZFCzhZmY5QfPv8fxJxenixRTGYiOmTh53DhSrVqCKxqbeX4tpUyRxDqDW3POCJmOfosz7koOmegCw+HAyl8iBPgM3k2PyCNwOq/LOS3zxOWapHz++9mAe05el8ColHpU3X0YsM4D4HkQXN77fL54gePhFEhLL1J+9LpKy+M4ynI9zo/FPDzniMAjRACdqVTCywRxQtPSGkgrQ2sqVmbNxbbBVhbnBS5A8JkcI0ZmmnWFbUubY7tRrCtYVVBZUCJCGJHZo4WnUqloLYtMpRSrSnG5qVmtN0y+CIr0zhOByshjwsiMhnjvORx6um54TKSREHNgnDyruiIL8GHEh3mWOefjulWqyGDmXOYHnRc0NAWBQRGdxPvMdBj49u333N488Oa71+QcqeoKoQW2rlitV6DELNXHce8RUrJ7eKDvO7LISC0wyjAFh8+B8dDTMnsjyHLvGyso3Yb8bx4OffvmDW4a0bpYBeWUsNawWq9Yr9Y8eXrN/rDj22+/5us//xP+8o//NddXL1hdbFitW5xz7B4e+Pa7b2kvnmHrhrpdMQ0Dzk8IWWHmG8A2NYKMcxPj6IkZVramAFiBfngghMAwDKWHIzJPn10jkoGUaGbLJykl2/WKwpx0SFka0Mc+X4Y//s/33LwYyWyQuUIgcAwYozBWUaGQqsgmL0FGUTzAyOCjRIQB5fbIrGlXlt/5/Z9w9clL7GbNV2/2vHmz57vvd9zsK3yQZClwbiSGRJgqQiyyXj64+QYr1UxKuRhhzuSInPQRtompIiNBWHKSjH3z6HoXF4Si9q7nPguJQo7JYISajS0ll+siafTq6VOykgzdgf3DPX0/cHv7QPAeazTX10/QWtH3HX/yJ3+MVApTV2gpqaxmtW55fn2FURqj7VzJijmgF/WH2kqUynRdqcxjSigleHJ9RbtaYa1GKgEqghJMbjqV3UJQWYPRBSVg7ikaswSjXBT7syDISDUzEyfnihywkFxeXuJ9ZBgm3BRm+HsJroLkSoNda01TG+rKcrFuuVivMEoTYuL2/p7dYV+qn5AYXJgl/DI5piLUfBwuLB8+xhJgffCzHc0H+rhnCGfKZQDcaMPVixc07Zr11WWpErRhEQPMiKJtu3wHOAbynDOkzOr6CfXmAmsMh3vBQ444wKxa6svr4mFoLcKnIzmh7B3lf0vgKC9eZFYzUWpWV5f4GKhX2+K40vUIqVDWPPpaSqk58RBFalCXRCrGMAtBn5xerC36rUvgklIWQ9n5Nay1hBA+CrRLq2OapmMgq6rqGPSWStBae/x3nlsoy2dc3m8pqrUu3z/NtmMfCt5U1lBpiReBusqoWmE3K9YqcNgY3DDwpFW82FR8cnGNkhRFpiQJIfP2Zs8wJQYBSQouNy0//fQSnx1SZ66vGiqTMDJyuD/QdwfuxoXQFcE7tJoThcmifcZmydo2rBtN02653d3TTyPO98RkycrgQ1n7D7sDr7+/493bh1OgyMUA3AfHYdfTVBeApB8d0xhxLmErQUIQIuQUcVNiGke872lbzaf5iqoqrbPDg+ZwOPD+fsdduKFTB4Z8oLY11apmc31Bu12hm9KDVlJSVUWyUgrJxeUl33z9Dfv9rtyTRqLXBt1ohm6kezjgJ5hE5ukna+pKU9eWTVvT3z8WPP91x48OgloJKqMLK2wW3F0WmpgzvLqqubq4hFiEfC8vVmyvLmg3a/YPtwglqdqGq+sLqqZFm6o4SU8GJeSxpR9yybS0MYRuwPvAdrtFaYFUIEWxHyoWKQkfHNPUYVSxKFnclYsnnqCpK16+KAy1w+FwVIMBCEEQggA9O1FkiRSRLApkkMhoLYlZzCV7RkR/DCguK2opaFeGV5/9jO3G8Orzp6AU0Q/Ubs8q9myyY+8NKQgmVAl8CcIc7PL8X2bCQ4zl86e0ZLsLYibminH2tBOp7E4fjHRKNUu9yQLLSSGL7FAKJTiJsvFLJakDhJC52G45zA7UY9/jpokUA69evUDJYnTa9z3D0GO1wlZV8Z5zEylKtJzhOcRcjUWCj4yDm4kvjsnlo3GvNnp2BTDEFJmmkRhnJXkZkEYxTe5xNnfuW3Q2IJsSRc0n5CJDFfJsRCrBVPi5F7gICrvJnSoGKea1l+b1LGbvQYW1hnVreX61oakspMy2Uez6hrtuoBsddBM+FB3FQLmZZ3QTMV8bISVGlSRJCgjBc//+Q7H3pYgszFkhJM1mja0bTGORemapyiX7Dux3h6P4g5RFnP6kp5vxM+xnYqT3AZdBz+xQZCQMI8p5xsGdzEw5CTFsNg3GaKzRpWczr5t6u+LSaGxdMU0TMezoh5HDYf8RGWUJPMXXb3aOVxKdFziyJDDlOmaSSMfz4b0/VngLOrS89lIBLuzRc3hzCWzLiIYQAjejGcvPUikuVeeJPZpBpoLUfDieQ7k+93d3pEaj/MDVtvhu5hBY1QKRDC4HGgU6TbRWYLSkqiSTC4whkP2BMEXCmNAqo4TAqEAOEyLlmcRWyEyjL84uk48YVfRdzWwGnHLGDYHuoeeuukWKBlM3UDdl3YmzHm9OdEOxx3r3/r5I631oqpsVRilWjUDrss9rY+dllxjcAZMkOauSyLgi0hGCxDnNej3Q1JHKGKZe4XwmEJG1RKeix9y2KzbbbUmOZFEVKypipSuYUjn/x/UjNYEZAhdg64qcBdZW1JViVSsuVpG6VTSNQcs8V6M/ri3444kxWlFZw7ptEKKI9RbYYiFuSIy2rJoN4qpk5pvNisvrDav1hv5wjzKGdr3i4nJD3bQUCVdRYCtmKrR3xHmRG2PJqWyeShZ/O6Wh2EaWcjymiPe5ODbYGmNtgb9YNqFSPaw320KeEY+b8TFKQhQInWZdz4xSkegTMkqyEISUsSiEmtPhUOClnASeTF1nVivLb/7uMy4vK5rWsL97KH3U/gE7TbRxoooVIQiGJIhZEDKEtPggctyEjqSEnCm+Fo97PhmBFOlI1kkic/aVgBIEpdJImYrnmSxBMGWOYuNSFgZbZSUxwma9YdrtmPzAOA4450kx8OL5S6ypiDFyf3fLw/09TVXRtC3NalVIJzFi9SKZJ0tA8IFpcnTdMBNSIqNzQPlu7Uyzl1LBLHqslCxEJBkwlX00cwYnbuESKMiitGRjUbLPIs/jEhmJQKuiZh9zJs0OC4sB6XFuSwoSCZkL03RRyZdKoXWpci/WFZu2QSNZNZrtUJPf3ZZK0yfyrF0rl57XTLxZenyIjNaKzbpBiIybfs1s6gLNCYFt23lzTmffnpkMFuj6jsOhA4r0oNGKFNKxGlwYkypExhAIKRNiYSZlGfFj6blqpmOwOVcpsrYkXHa2C8pkUOVzmdUKZsm/GAKHw57dYf/oqywb2SJyXSA0NZPI8nwPFunFvCSvIj2CLReY/RwaXY5FTec8CC5C2ueeo8uoxfL3xWh3qRYfK+RkEq6s64VJfPa2JQjeIpxlrSKNWdEYiYueSmewEhs0tQIVHZXUVDpTa0n2gYAjh4HsPclHpDRINBKHyA6REjlK0uwr5EIobPgYqaqqIC1S4n3hOPR7T78fuFf3aLOiWgdaoyj6xGXdxJH2HqYAAQAASURBVJTIOdD1I7t9z839Hi31IwJR+XISLQ2q1hhVbMP0bOQrZUapYgKeUjEW8D4yjpEQJTFmDoeJHDPZZvzU4j14EsIKdNToytCsWjbbDUoXP88QI1oIZD5Z4xUUqTj1aG1w0R3XvdIaa0EbQ1VVtHXFuvXUraRpNSlMKPXBhvhrjr+Cs3xNXdcYY47Zlvf+SGCJodio3N8O1I2lWSnWW8tma1lvaj4XnzKNE33fl0CSIzF62kahdMOXX79BSqis5Gq9wWqJyon1tsVWmrquiuWRFDw8PLA7dLx9d4+1mrqpkFLgW4+r68Kem2nrXV+0IJvVGqUUKx9OPRRgcInBJSSK5BNEgRDLRgA+RZTKZxVMQolEDpkUMl4Fri4aXnx2wfqyJsaJP/5nf8Lw9gG36xl2PXe7HTf3O548+9tUZs39TWAUAockpAmRQERxqgLyibd4fhzZqnnuQSGIucCnSqmz7yWQukLZGi0TRlukVMgsScmVn1xm4qxZKh9L0wxcAKaqeHjYMQwjfT/y4tlLohA8POwQKbFpWj775JPZQ8zQX13hvWeYpqNRckoQfJkBOhx6xnGk6w68fvM1w9AXKCoLgo8c9t3cOzS06wZbaZp1VViC55plOeMmT04ZLSU5ebwU5d/lz4RY+tXeu3k8RnN/6Av0HIvqf0p5tnsxJeCcMSqbevZzUwoXAvfdUMxpc+LpdsUn1xsaK1C64tubhFAZbQRpdGUjJ5Lz7Eczf6YYEyF6UtJUVrBaNVSVPVZ+Hx7pGARhbtDNfctlTWRSDAxDhxDQ1EXwnFxgc60KQpJiJCtJipHhcEBMgdonVD+ha0Fr61lbF5ACa8zck+QYiL2bkKKQII5905SOSZugjEUcdjt+8Rc/55dffnlylzk7yrpO88iNn9sYc69VFIhtLkLPL/exf7VUcR8JCswn8Dzgw4nR27btjFgpnHOEEJmm6RgACwM9Hok0IRQrphDHQnIKkWkcGafpeLFSivziF3+Je7KlfXHBut7QWsFdNzLs7znsR55srqhkBN+RXYEP+zGhdcPKzmMPVrIOsxWWhv3DHogoJYiTQAkD0lC1CtuUay9iSXyhcBNEgspWRO+5u7lhP/Totqa5v0QYA0ojpKXvHYfO85e/fEvXT/TDxG//5heIqjqWTDnD5AM6StQZm1YpXxjYMTDMQt7j6LFGoY2mQlE3iqbVNCuDWbQ/UUzB8dDvcWEi+ohtGrbXF1w/f0Ld1CitZ+LRrKXs00zwy/jB09QVP/n8U+67B3b9ge/fvkNKRQwJ7zyibrC6JsWSKMU834Mftht+zfGjg2AInjDPKsEJjlhozN7HeXEvLLlEDH7+mzsy+oRs0bp0NVLKDFNGTYn1ZoVWkqbWPHt2hdUKmTxSZsZxoqp0ySCFoGlW+AB1NZUGaBalzzM5cgarzTELXCDEGNO8uT2+iXxITD6ipCKHTHHgWDTpZjKHEsXQU5cbV0tB8pCCIOgidGtqVSqKKXC4u+fh9RvG+z21UbQmoZ+0rF4odh5uQmA8wORL/0dEEOkE7eW8vP/cKMrLv+dDygIdzGMZP6QIJKRCSI1QCaGL+4VElfmwVHodUkmkslgrCaHcWlpr7AwnFb+2BcJSbFYrqtnVu12vkDOstcorXPB0/cA4jTjn6fuOafRMk6fvB5xzjOM4w6oNTbMqivtSFm87SmKkpKaqGi4vL5n8RNc/ZnmVXmmBReKsVyiZmbSU35cNLSPVAnEJhCjEFnJGLk1z5AzD5XJ+1KlqkHPmm3PGx8QwTXSDYhwNsrJHheU8Vx0hLP2teb7ug+C2aHs652cYP3/0mFNv5pykITguhSXNyXMPzRgkkKoKpeQxeC4i8ylFog9459m7e6a+x/UTpq5YZVCbNVVVRm1Qc+J4DMzl82mtPvJmK3ORJWIt/nzkTAqz797Z9yqzoEW8oLxOhixZhJLy8n1mGFkIObN6T96DZI7alCfWp5zZvnaGfxMpBWLM80xf6WUu5Ck595ySKKSmGAqBKadiUlu+qz5Wnn6uPmMIRdja+fPLg5883vnj60hREkotC2HGaEnZalJxWsiZmCJKVWilWTUGUwlCKmM1JW3KxfdRK3KWxDiv6SRm9EcWQlam9AO1QaFQLs7JXML5gTB4/H3Eti3KVGgNQzewux+4v3sgRNDasl6vcEdHlLJTTtNE3ShsXVpdmYUdn0AmtFHokNA+4aclYRdIJVBaYKyY21JmvufKv5tVg8wCrODp1TMut5cFmelHvCjCClopSPpISFpMCvw04SeHnxzTOCEQxdYpno8ZLfvnXL3/KCC0HD86CA59T98PtO3qEeNqgVC6boAsqCpLYsL7RN97tO0QUqJ0UT+vKsN2W4bgAW4fesx+5FWWWKNoa8MXn7ygMoocRrbbFX3fs98PCCRCaKxdYe2alC3D2JFzxPtIThPT6NlsNmgt0AiQurhDzwFaCvko25xcKeclAREUpNNgdNkMPEoLrC1Dz4UkI8m+sP6iCYQE2hZ4J3jP4faOt1/9ku7mht/8rVc8vb7m8ukTquc1t6PkQTl2X2a6cd4T0rwpLNDL3JMRzMPK+VQyHKuBXIIgWZyMYB/tPga0QaiM0na2glKIVMYxBBGhZFG1z4YQFTGWgeVKSpqmPvZxjFHUdcXVxSUagcgQF+V+URheLnjuHh54/fYtnev4/vvX7HcHuq44DyyVQLuqaVcrttuLY4Yf/MOcUIGtGjabSz559Tlvb97wsOsercOcSv8PNa89ZmBhJosIXT6TlIUNV/q/peo1coET0wyZn6AXrfWRTLUMdasFsUuCwXkOw8iukzQio6wtxJSYGIYS+GNMIGYBACHIy3D8/Jo5Z7puRKAeX6vlu5HnjPxs4Pt00efHlAxXScFm1T56bingSv9RACJlpmGk7zq+n0Z2d/fs7x6oVysAnr14xmazwtalN8850xRO2g7iGIpniLc8SjK7xMuy4StZYun5Yati2OymiZx1qZKzKAQtIKmlx1ccaJZh+eVNlZwRjuPyFkeIVUpJVVmUligt8WEi55KMSJlnNnc+LhqjFCkGyIngp+IaM587AWRbHdekC8UMeJomhn6gHz9IxkIm+XhSHkLRNi11dcBZT2UVSmYgEhLIlEghUDcJrQSX24aEJIs54IXisG7rCqkVCfAeCJHgS9KWZGKaneCruoySKA3KucJQJ+GSw40j3h1YuS1V3ZIr2N/teP/2nvdvb7H1iucvr7m6uiDNkCTz2ukOPe3KcHmxxlQFUZA6I1REqkTTVmX9RsX7+31JHioDIiN1xlaC2mqMrFAPxWNxVa15dv2MxjToWEbcNqsNrp9wqbhd5MtLrLVEY5CqrF83OfruQL/fs+t2HMYe1/WFYR3zrH41qz5JCWK29ct/lRD4VwiCzDDIfr8vLCs9z93MjvEhuMJIrAVG6pJRpogU5WLJuYoSAvpxwu07vn/zjm7fEXzkN376m+wPB7775jvub16zXTd89vI5UpXA+rA7zEytiMBAlmw2FwXCiI4Yiws8OTOMDmMsTdNQVaUf1g8DiHmkIS8XHQ4Hh7YjcgrIXJzXc5oK2y8XaLQwy0qWI5VEi+KtlSJEOXF/J3n39sCFnFDTyFXdIp9eMxpQ+UDyAjeC6T2tsPzW0xrf16y05i/fpQIFKYWMJ627lHW5NQUsw8Xn/Zp83J3knBXPG8d8KGPQtkKrjLY1RmmUKGMrMs4VrZJoU9HvJ7p+YJo8LnpCjAiKqkRdrej7keATaiOp25bKWCKZkBI+Rv7wD/8Vb96+5Rdffsl9dyieaC4wjhPT5PA+oLWhrmsSkWEcORw6rq+uWa/XDP1w9JYMU8fQZ+7vQaBo6nMXgtKs1zPBIviS9fsU5/6nOlZXUsp5nEPNgaycMWs1Ocd5LrP0w5qmSEkJIRjHqVS+SlFpAbn0OkKGKYFHE4dI7Afev3vgoRtxU0IJi1SlvzFfFkSWc3Up50qlDPQPo/8IBy1zWLGQP+aSVgiB82X+aZyNaUt8zjOCeSLWqLnkEUJia8vd7R3/+R/+K7768kvu7+4Zux7nPMF5pDFoa6j+2f+TV5+84sWLF/yDf/D3WbUttrLHcQABfAgyzG8/30PFVV5rxeX1FVdX19ze3j3quZdKsiANYh6PUKqgLimVXlVRq2FOY5b3LsQvpQo0J+Ye6eJQYufRrKoyVE7jrCK4gUlmRI5sVg1VVeHdSJr7g+R57jEEwgwxn7M+Qyh+o855xmkieM84OWIIj5wxhIB102C1LmMh00SsKqy1vHh2zeVmXeyjYijfMwNCoozBRwjDxDB2aFNhbI1ShpgFSUS6ccCFwN2+9N0rW1FpWxIbKRnHiSwkWRqEKONI/b6DFEBkbFMhlcEqiUWhYyJ0HbgJlTNtVWOrmkpbjK7Q6vHIWN8FxjERI4QcQESkzehUdiExTmibqNtEXUuc80yux7mWGBqMarB6TaVWrOsV67bhi3bD88tnWGXxgz8qOU1dkVD0zmGEZDIGKee2EGWN7XcP3N/fcXv3lpAT0miUNCAFKWlSTBwOe1ajRyiw1ZwMf9BK+nXHX4EYY47N5SKvtFQr5dMKkRFSFPr3sX8xV4wUqABRlvhu37Hbd3z19XeFUKEUlZYccmIcBtzUE4PjarueYdWAD5GcxEyEKDNGRutizEgi5XC8QZz3ZARK60KeEYnop6I9506QLpS+lZsSgojKxTAypchxjGvOcEMS6FDIJKVsLxltItB3jt2Do1tlquDRQhU6vdIc+h1BapKu0SuLMJlLq3i+rkkB3j5kxiCYYsmtS1lzloJ/dJx6IDOOBJzkx5iftciJKQVK6RIkRDlXUM5pjJm+n7i737HfHZimCRcCYe75lBsP+v6AFJLoA42xtHWDkJKh67jf7fjqq294/eYNX3/9Hb2bCClhjS0kFO9JKRbYxkgury6RUnJ7e4cPHhttgcwopBZrMpUJaDUSY1XKvnNkUJw24uOMWMqwkINmaK3MhZUNWIjSZ4ulhOT8JhGII2kDSlCVAvKCGpBIZ0QRIRU+ZCYfGEaHd0X3UMmTyglHMGZ5njzKlS0MyA+z1ZgKsWcxbjlBb4kYC5EHyvpbenHyvKmoNRKFUIVMst/v+Iu/+At+/qd/xv3dHSKfeo1CFsIXAh52Dxy6A3/wd/42lbXUdUUS52SQs5N/xsw9V08SUhwVnpaB9vNjOXdysdpCzLqnRWhCAkhRgm+Zw2C5mEqeqj7x4euJE1SsBFityFGRjMJohZKCFAKJdAzM571JmLsDZwPzIcxD4ZMrGrNhQTHOvg+iqF9piZJ5hmElUmXq2mCUJPlADJlEPELHaUYOEHPwjxHhI6SID4lx8gzRM82thVwMRqmVRs3QczEVL/PWQsoi0uECKYUCXRo7z3pmoklIIjE5ckyoBbJVsiSC8/c77iyZYs47BobJUQeQKs+Ji0SbjDWzcboUrLcWN0kmn1m3K9bNhlV1xareUNs1edOi9Yp2e83V+gotFL0cjtc++ICcr00MRcIwU4hWyzVZNEuHvi9M/VwXdEEqosykWBIW74o6DlhETh9nb7/m+NFBcHuxZbPZzMSWspi1LpRlH0KZDVEKW81zhDmzWjVFVzQEri6vyUIw+cg//xd/ytdff8ef/fzn/OTTT/j0xQuED4iYIEn6cSTGA1+/focbS0M1pZmcU9mZYShQqkAhGU1M0DQ1trLc3t7MvY1EPwx4F7i9uUXrQpOO6UR7Dk7gJko/KnlEFsU0dF605VaMgCelRc/TorQpYwh55PYm8dUvNVe5Yi0nGDNdF7i9d/zF6z12Desrw9/igouN4Gob+b3nkU8vFG6KfHsnef2gyVKXzVIEMsXcEtQHN+GSdhxVIIuSjjy7lGLu7RmDVoVFpaVGYUBIslS4IfBwf8/r77/nm2+/pe+Guccmy3ef1UNCSnz3/fdM04TIiX/nH/5DLi4usW3L919+xT//l/8Z/8l//J/SHXpyFkQSQkvsukXICakVxkiatuHq+op/57/97yGE5P/0H/4f2XU79t2OdbuiMgoFfPFJw+W14emn8PM/3XO4353w/VyGv6OICJVIsw5qShCzRCaJFqY09aXEWoUxCqUhjI7JhblPtGyipf9XILwwbzqh9GAS5GwKEiAztVE0laY2lrtp5P4wcRgTIRXmtJgzWIGcWwS5jKGIUtMU6AuQCamLE/aS6gCEkAgpFYbcUhOdwaJKnTEYZTpWg1Dew8wSXcZadrsdb9+845/9J/+MX375JbuHh9LCOGNBLq/9/bffcvv+Hf+tf/gPaduWzWbDouTymIl5gmpPf5s77nNATzz2MoRTgDkflcix8AiCL8a/KpdEIS5IB+rIOi8ojCHleoa2S0ApbNLENA3kMJGjo5KgraWSEisoGrB5EdxIxyF8KYtqz/JdhnGcuQt+Hksqcn3nqMKHhJzGCjaN5mqryGnAu4kgC4JgtSBbTfKC6AXBlSR+dA6ZKL1qbRk9+H4gh1JxPuwORVZTUhjNqYx/NbWhtqXSPPR7JucY3Ii1NUoqJp8JodyrQ3KlSgLcVMTulTKkWBKVti1jF0O/4/37G272/RkyltnvJ6TdEwkItaZpimFwbRRWGbQ0R7H662td5qWj4Hr7Oev2ihfPX7Jdb1k1a/x1jVQVtlmVcY9QkmEfCxNctOVeKUpdJTGIIdD3A9M0sT/s535vnKcAPF03cP1UY2pF0Sm9Z3A79ocGXa2RooVc9Ft/7PFXYIdW1HVdqoVpwvtprvqWbKywiSpbl1tYwtXVBUaXmzOGhNQaa2s++fQztF0hdMXVZstmvebdXUfXO4QwrFeWpq24uLzCT57gPf1hQCldFmmOMxHCHfsDITKzvwI5pXIzWMtut2cYRsbRcXW9xdbVI7hGSYmSkng8abOx6XyPL9lzyZoKdBATGCPKfA+Ch4fI1991fFJlYuVpnWf0gSFmRv0ph7Tlpr9m+sZyvVb85FnmyXZirQO/9zRRRQ/TxLeuKoxRqdHZISms1YVoIT5KbmY47AdIFlrr8qNEkYGTGi0tKRYY8bvv3vD27Vu+/vrrcg61RQlJypKYYJwmXPCMbqLrCrFFqxJAkYrvXr/hL3/5S/74z37OoR+JCbTShDCRXOkPSlkkzq6fPAUBu/0B5zx1U/P02VNu39/Qdx21rdlstzy/vuazT9estxlTDfTdHXe3d4824xQjSUqyLE4jGj1XeqeulVaS2lbU83C0nDfPdEx+FmJF2di8d8iZELVIhYXoCClQWcmmMVysay7amtYqHkizhulMalg2SSEwuqAN3oeTwgazW7qUhcUp5iB7dr3KJhyKYsYyr3f2vZeKsrCA05HUAWXzijPRSaWF1l/GJBBinhk9jX4sz1leF07KNz8kIfarjoIWlOcujEtr7QePKZ9xCUA557J/zIpJIi/zgczXpChGlcpc4L0rfSlVoNTyorOb/AzJFlNhiYgSUlFLEbNqkZIlCZOpnBM/j+LEWNoiQsDkHDGUymgJ7UvrvewH4tGeIQRUClaN4uqq5eLC0laCSsWltMT5kegiwcWjm4rPESVUSdCqBpdCGTMYI85lQhBsLy+o1zWbqxZSQOR4FIiwRnF1ecGWjG3XaF2Rk+Db+JrdoafrB+IwoyJKYNQsJRcdLgpcmkeGyCQ8t7c3vL/vz3ALMFIWKFUqrKqpdcWqro+zxilzUqoShuAy3S6i85bsV6h8iREXVGaLqVVBP9JpLVtrELHMBsZw0jpeZjLd5BjGgb7veff+HYJSiU7DWKDqENjv7hjHAylMaDOhjSfGDTm0qHwN7JDisWjDrzt+/LD8vKlKKXFuIqXIMA87L0O65XuWIW2tJev1ujS2hSTFIq+ldcWTp89LEFQWIxUKyc19T4gBKTVNW9OuauqmxehI8J5pCI8y2AKDMfdfygKNIRIps0ASMFofG90pZ2xVYT9wJj9mx2ffVUhZBlbni32aRYpzMEqIHEGXrL3rE++YuLlQqCZgcihSaQmSuqTPV3Tumv4mcDcUGbZKBy6bwCcr6FrY1fC9F6Rs8ZjF/OjRTnnSSlx+sQBvH7OhlFIFDpbMG3WZG0wpESK8eXfDm7fvefv+lmfPnlHbGqEUPlBksZI7QTSTI/qAbkr/Aql4+/49371+w7ffvZ6dPxRaC+LsgOCCw1pDVVnqdoXzE93DPYfDARC0zYp7eV/GB7xHScVms2G72dK0kayLq4d37lEwKEnQQmYpkJfWqsyqzdqpWkpqW0S05eyM8CHh5HQu05lGZfnbUX4LSaUtbWXYNBXrxh5lnbzzLGzKc3muLMuM5GmJnRh0gtJTXiy/TtcVwmz9FGd0JZ9fZDjBiXMFy0xMWvrEKaVZ+3MJZMt3PH2+Y0+N07k4rv1fcX6OH0Gcf95TFVhgvnxMCj6cO1teZzk/KWW8dyd5PZZ5xswiGl/ad+VzhOALg1pwZA0W7d9TO2CxwcqiEHXEDNfKciPM2r0Z58Kx4itBMKOW+cEYybMdW1567eXMc9Q8PTuMhqZSbDc1221NY0EmR3CO4CLOzwhWSJALS10oibIGZTTSVqQh46JgdJHoEikWYthqveH586cEP5L8hA7TccZyvW6R2rC5ukZIQ/CZt6/35D4yuBE3u10Yq8v8aspMw0BEEoVENysk4ELksD+w23XHPUYIQdtYVnVFW9XUpqY2Det6jdEWLVWxFStdKZTSjH0k9AO+t/igiZMheksK9rhfhxDJs/iHkKLITubTDGjZoz1hJiL1fU/XdTzsdsh5Pjy4sbQFvKM7ZJSWkCeaVUYZSQyKFCpyWCHkiPgr0F1+9CO990f8WGtDjIKb9/fHoW5rDNYY9s2Bq+sL6osNlxdPgUX8GZRsWDWXPH9Wcf0k87Pf/l1ef/+Od2/f85/96z/nYtPy6uVTLi83aCMZhqEoyaTMOA4YY2fpozIWIKU+BqgSjEuWtj/sCuFhvjHruuHq6ilX11tyPvUHyg019ybkWa9hhq5yWoZE5TwuYMtiluUUq+LYRu8Twz7xJ7+857526HbEDpknSTIyoUl4LA/hKQ+7yPf3e75643i+jvz280CjHD976rlPkdup5f1kEKwBQcwjSx/vNEiaQR2bNgUifKRsIY7nSksKIUZoEIrbhz2vX7/mD//oT0EIXn72Ez77/DOsrdjv9jzsDkyHnsNQ2MCHrqPrB6qq4ouf/gZXT55hbM2f/OnP+fb71wxuKp8zRfwwEEilyZ8zfhwZppFvv/uemAKH7sD/9f/yf6OyVZH+SonG1NzfPDAdRu7e3hKmlzx/seLz377kZz/7Gyj1iv/o//4vSvnNzLSkMEMrq6mqimdPr+j7gWEY6LoJo2ouLi643F4AYM3NMRCeVzzOOaQUZd5pHpKPMZd+aQhsV2suNw2fPL/i5ZNLtBB8++17vnt7w+ubHc6fV1Nl4z7d0BEp9ZGxWYS5ObtmkfNsZpqK7JeSzP3ayHlFJo8V3GOYZ2GeLsPf6qiDWUrNheSyVKrl3fKjoJc5raXlezw6fqC9sjykVAQSW1UMw8h+v//gkaee3jJK4lyRLlxaFplF2WT5dOLYF+q6/axsVJJjhMB5V2DuOTAk58g+4yOAxNq6XE+pyFIRgytw4/39cfDeaIUUM7VHSowSSFuVnmBMaKk42jbP1fLxGwnYrmuuLtc8eXLB7/yNn7JqLP3+ntfffMNN9573t3dYbVlVLcZU6Kri6faCJy+fo63lL/7sFwyh56EfCSNkH0kB7u73TCmxvtyUvqM1hWiYI94nqqpFVxVVtaIfPYfB827n+OXrB37xzfdUTU3bWJ5dren7iSF53rx5jbY1tmn56fPnJCE59BPDVDw+l8MYzX/z7/9drp6sqariwaqVpjbziJWQIDNCJCDSj553Nw/88//4z7HmGZXZcPtuR9OuaFctz55cz3wERQqzipHIjOM4s25HnC+jU7v9vtw73jO4ghSEGJAiIUlM454YHD5O5H0pJJoGoqgRumE4CHYC3qnEaptxHwhN/brjRwfBxactz5h5SlDXqzLL4YtrgBSaGAvNVgpB349UVakgjaqpqpbKrtCjIyePSI5KC+rK4kOYWYMHLrbNrCLDUf2hHwY2ctEhlPPnEEcViBjT8Wa7urrCWktd12y3F+ScMaYuZA3/4dxZOg73fpx9c2b4WjLxIh2V0DkjC1+KKBRRCN6NkRQkF15y4SsskbWM7OMD2SdS8wVRWqbc8m2n2HsHBGpdZg8vZCIrT5ADXYIwk3QWibTlyHkmJYizjPiDjaucCzXLzJVGwzQ53r59z1/+4pdEBNvNlk8+/4KrJ08A2HcDPmZ65zj0I+MwMk1udvYwvHz5iqpu8CHy/vaW/aErbMhYemESeaxIFzgvicz+cABKP293/4DRBqMNeq5CUoz4ydPnnm4/MmxrFDUvnl+S0uVMUOHxNaGw+aIu82daCYr3bcRaw5Pra6qqCHBv1mse9j3DOD2C/BYT1UVmSyqJmDVmlYKriw2Xm4bKaJwP9CHy3c2eu84xnAXAMmQdjrD5Qo4RC0PsuInO783HgWap2BYRhPM+Tc5nvbY5mz7+e37/oxOGEMflkBe6+FLhLa9xBjGcizM8Vk35gU1kDpin18oz2S0fkwo3fSCgzWlwPcRAnH0+Tw9YeoozGWZe0+WzzNUqsBDvzj4MKUW6/sCw3zN1PevZW1RJTcyQYyJGR9cVcoWfEaGjIsnMstLzfKjRmhF+UCrt8VEcL5RWpJRxvtgATpPAR02iol0vGrozLCclQipWmy31asX26o5hiPS9577rSoIwTShXIUbHu3f3VKbIQDYikGIoDPyqRtmKdkwcBs++m/jq+xu+v9lxu5/YirKOY4xkVRSMmrZGGou2usx6i5IQqg+4BEIItuuazaqe7yk9Q9iJnARJ5HnNlb3HTYEYBLXdsF1fUFcbtusVSQjGceTu/g5mfsFhdqHJORfhE20YpxIAu74rWrze47xj8q4kjZTzaiRMbiBGR8wOIdMsaynwAZwTTC7Q9yP3d3e4eM9ud/jRUfBHB8Fz4dkYCx6/3VwwDD1dgqpqC00b2O2KBcbD/Y7Lyw3WWppmTVWvqOs1en+PTx43jGiRaasifD0OA3e3mWdPLzCmLMwUl4ZoR9u0M4NNEmNimsLxBosxonV5zpMnT45EgqpqZmhIIFVmcuPZtyr9xZSKIPaMmrB4JRUSwjK8K+YbJ5FIqJzROYMorNeI4o2Dw5Sxg+Z3bMMzKdioO+6m9+T+NVQtUV7Tq6f0g8b0iv0w8Goled4qLslo6UnqgM+RkDWFMDdDaefN+ZlYwUwQ/PB6F1hJzUQQRYqCvj/wzbff8Ud/8mfYquLqyTN+42e/jTGGcRzJb94z+sChH9gdOtw0FYurkLCm4ovPf0JV1YyT5+37G3aHQ0EHUgmCWp0tp/kz5Qz7/R6lJEZr+jlBqozBKI2eK7SYAy4J+s4xDQlBy6tXn1M17vH3nlmNgjLuonwJflrNfqQkmrri1YsX1JXGh8B2e0Fzv+fQ9ccKa0EQoFhVKVWEe0clybpsEM+eXrNtDdZkumFk30388u09vYu4CFaLR0GwqJuc2ISLl2ESYvZvE8eAkz64YkLIWYP3JE92HgDPnRzEDNWJsyC4wL4nVaF80gKdv++RSDOf8+Ol+lV9wAUaPbuojx53DsunjJsc4zQ+eswC2S7np/Rl8wlizZksZhWUDwJgSXiY70WOSWjpd0ZSTNze3vLu9Rt2d/f8zs9+m6ZpkPPQe0xx7iHt6Pp+DqYl0Vg+o5SFZW7t7F+aEpObv/t8g/1QwmIri1SKyQd2+wk/Zfp9ZHSaLFsurxuWMY9p6Imlk0uzXnNxdc2T5w84lxmHwP27A35mOSrnSYPku+/fY6TESMnGSmJ0TNMAxiK1oV4f2PeBXTfxp1+95nbX89BNSFvmsVP0ICRKCy6uLkAqhNK4aSQLBam41Wh91jsT0NaWprZnMHma3XViadCIJSGBsffkoLjevuDZkxes2g1t27I79OwOHbd3d3jvmUbH96+/ZxxGckp8/ulnPH36lMl7uqHnfr/jMBQPwWkaj5Z2Qkgqq5BWliCYJrLwmHlNpAw+ZMYJptFB7PAuUY333Nzefbyef8Xx4yvBGXLZbrfHJnZl67nvUxXIUClqW/HwcIsLgb/88pes2oqmrdhun7O5uOLJs5eQi3XI0B+QGBoNn7y4YrffsTs8FOdwY0kkfMq4mMhC0Y0TN/cPfPbZZ8WeJybGcWQcC17s/UTXO9rNCplLVZJjIoUi4ixVph+GR4s6yUiSETDkXExgJZksFVkp3GJHFAWKeZBXqFPSikCR0UBSa6JqudnUDPaWJATVzS3GHxAPt3TR4JuXcFEhpSFT8yZesO8834yBjTsAxedOKlWcK6R5FOUWo12BO36WKPVjduj8uBRzoQ9PPUM38vM/+wW/+MUvubt/4N/+B/+ATz79hM3lFXe3d9zd73jz7j3vbt5zc3vDw+6e6AMpRIzWbNZrPnn5kqHveff+LXe3NwxDx4mYkBCEmXEpZlNbOfeGW6w11FXFqmqw85iFUaoQF9Ssdxkiz55uECj+5I++RNt77nbTI6g3paLAIXJpmPuUOPQdVmWuLhqePX3CT3/6U37v3/pb/Mt/8Z9yc3PDzX3HYX/AT37uixahbe8igqIKomtJKysur1Zs2g2bdk1lMqMbuLnfs+smusGzd0Xst6oN0RdYT4rS44uLEIFYZEqKFZGeg2GMGRfKQL3kvHcmkFoilDo51rPAhXPA9mczpGSUKBB9XddF2SM9hvmPCOesEPToWOYDFkbK8vcfCoRnv1+A08VUeOmZL/dT9IE4hUdP9b44PJTE+RSUM+X+EWKGhUU+EmLMPLMZY0LrRfGn2PWUp5/g7G+++Zo//9M/57tvv6NdXXB9dc3FpToS+Ha7u5L4kI8CHaX6K2MUq0pTzbBfpoxkaCHK/b/03/O5em/5zg/7A1o6Dl3m7nZES40fIqtVTdOuefH0AvCkOPHQ9Uz7njc3D1xdPyX7RKUll9sWXl6hs2D/0HHz9o5kDSEm3r++hQgyCy5aS0oB5yc8gFJUbc/NrufuMPCLt3eMPuOSoJkcl9uKT15csW6K9quuGqQyCGHY7Qf6wbE/DGRlcNNZ1ZspfdHojz3s0h6VJAICSW02SGkQ0rBuRsQ2Ep6nYn5rDdfXl9RNS9uu+OM//VMeHu65vblju93SXl2hlOKnv/EzXr36hK+/+Yp+GtjtduwOu9Jyi56cA5CPmtI5C0KcyNkj1EnPdcHiYipzttPU4ccdSbzh9t17fmwp+ONl0+YMumma+Z4QGF30D6UqmbCSCqUl2hqSS9zfP7Dblc2qqu/ZbC94dnfPelN6VcF5lFEYLXn1/AnGSLhL1G2DbWpi9EwukLLA1g0xFciuHyeaWlDZikZKtNFMbqLvi/XO5EsvsEiSRVIMODeVWcUPaHkZymIXqvSpciG+HCGjecgWASKVZrsUYIUqprVCEAGdEi4llASnDalpESqhbsHkQBVG8nBDzBpR3yPEmiwMQWlykmUIO/kyNyMlMatjxr/0cgocXX5YhLXzTC2O572izDCMdF1HcCPDYeKw6/j66295eHggpUxV1wgh2e8P3Nzccnt7y939Hfv9jr7vcNNYZndiZNWs2KxXrNuWw+6B/nAgxoCSgrquMEqihCpjGPPoiK2Kb6Cxms2mmHpW1tLYGqM1TVWjpSqbkVQEH/CTo64tQibGYQTXc9if2GtQ9my5oIW59PC6bkA2lqqxbLcbtpsNq1VLBibneXh4YBxGYoxHdqSSErQpcldWlv6L0azahrapqK2h7+4Zp5F917EfPINLpTKfP0vMRaS7KMSoGcWMJwLKXMlIyQwnzVWckAVaKmFkfrWZ9EERVV+G7hcN0LTMcwlBzBlk2bCLOsq8RhcySz4FqcfH0ruc69BHVd0Jpn3EHD2DYc9VaViqXWYHjhiJ3n/kLJ9mOn1Zn8vzHn82sUAa4lRZl/cqkmZHGbg4k4Fy0aZNKeGnkWk8MHU7prFn8mtcSnTDSH/Y8/2337Far6jbBrOQaObqrzKKykiMBClKgpJjJMeAkMVJXszNy0cwcYZuGLEqzG4lDRJFt5tYD57VKmBthVIJKSIxS1IWxEiREuxHYvAoEpVVtK0lhcC4bhiRhMnTdRMyS7RQxEYSk2D0md4HkigIyM2u56EbCwFGSIwtVkJVbcs4T12siapVgxCGjOLQFRu8aZoI9PTDOTIGmZKsLFWwmIXOiwSapqmvIGtilDPcHjAmHVsWSsqjJZ6b5c5IiVcvX7DZbAixjKp0fcft7S373R43TRiti1RhKONCMQVi9Pi5BRdTAFJh+Rau0ZG5nzKzCAKMo8P7gb4b/833BPv/N3v/EWtbluZ3Yr/ltjv2uucjXkSkqcysJItVJEE21IIgQIJGAgQQIqRJjzTSSCNN1cMeCJq31NBMUwENCGihDUVJQHeTrGqybPqwz1537LbLabD2OffeyMisyO4i0AJqBW68a47be6+9vvV93980DcMwcHZ2drxRpJQUpSGEks1mMzY+A9W0QnaSL774nJuba9brNbv9nrIsOT095Xvf/Q7n5+d89MFHzOclVVXwh3/wI97fXPPFmzc8evYYYwxNnWgTLgpOzh+zWq15f70iCMXZ6QkvP3zBfD5DCkFRTVivb9nuttzuGuyoTJ+FARHSYl7mi2SHcx8Yg8SjiCJx6FSMGMbegQejExpKioCODh0ilZCUWUFucnSWJRK+dez6Fi8EngIxO0WXM/SrX1BFz0ns+XL3ltB1IKcE/Rhh5ojJCRZFEJIuNxA9IgQMSWtP4tLSI9OkjJFxQUk7pIRU7OjuZbgxwru378i1wtme9e2G1c2aP/3Tf0OQCpPnxBBZr9a8e/eON2/esF6vefPmNfv9jq6t6do6camc5zsffsiLp49ZzqZ8+VnD+vYWIwTzScVyOaMqc3KTUeUlVTUlywoWyzlFkQxyy3KkKghBHPX+tJRomYAcCpGg0U2TBLCjx8FYQno4lWNIUlMQEhfKBt69X8PZCYWZMJ8umVSTNCdGOsybV2/uOPciojNDWeVkwpBpzaQqKKeGvNAsZ1Oi9fR1zZeffUk7DHQh4mVGjGmjFH0Cugx2LMGTxAgUHoaxH8ihcpt4b+nqJamx+DXpvjQPk+yvj56m62i7FsYgZ4ce3/fj5xf4ESVqrcI5d8wIlbgnyHasHNxbwMcqaPR3PclDMDyIGH+9NHoou6addyrL3pcDkIIE/x96hrbBts39qzUCjcKxbRGJRy58Yq4cwGgiGR/HpLEZRjpD27VH+T7rPaDwITm/eA9KWCplmWcDwe2xbkrnHVerW67evuPf/Iv/kh/8nR/zwUcvyWVCFWZKM51UFEYwkRZIPMKhs+Mx1AhVHlVaEImneTyqGLldrfGDYVppsjytiV+9XRH9NUrC63fnzGcZ87mhKjQ6n2J0EnZpdi39sE4VCOnJTSRWGfF0yab32NCy3bVUWYUpM6rZkq7vcL1j2w301mPxbJqWpk8mBmWRU00qnj+e8vikwGhBWWqK0lDMyyM1SjC2l3YNq6bhctc/uN5RpJmYLu649kmJkhW5mfH44ge0tWOzrml3W9o69Z2980iREN0hJF3Ppq6JMXB+dsL/+H/0P+TJkydcXt/yp3/2F/z5X/4lP/vpT4hEsszw/IPnaK1p25rV5oam2bNvG6wNSBkg2tSLJG2ARBhBbD4cRVCCCwyDo9kPNLtvj4z5nXiCBw7Qoa9SVRXWWrquo2kapJTMZjPqbc12u+Xt23dsNlvquhlvhp6+u6bZ90yqik/PPuPp0yecnJwwmU5pbY8fBra3t0gl2W13ZFlJcXqGteFYmvA+st42+C/fY7KEXFJKImJGXp7gNqPyvIWiMGjlsH7HarWiGevSdxN6vBEBExu0q/HrPyO4luh7VKnJlKJSiidVxSLPeHwypagmmLwgSo1zgW6wDGaGlRlbKTmdJpk1NS9QG4WMjjx0qVex/yVetXh9ShACl+XJjNQk93olJXrkQh7UJhCj5YwLODcqIsRIdA7cGmFXHK66EHByesbjJ08hevwQ2a73bLc70Apte376V39BPwzc3NywWq/pupZ6X+Ncjw8WI5Jg9tliyd/7/R/y4vlzut2G/foW29b8o3/4RwgtUJkmM8lL0AiDlMmxIpHUU2VAHvs7IintkFzvD9qTuckpc8Vkkhr31nuafmC129O1zYPJLPCHfCbJaY0L6WZbM3QW6wa6Zo+RnmmueP5oyQePT8mKkizLKeeTJL5NwPdJjFghsLZn3zfsbm7ItaE0GR+++CBRHqTkq7eX7OqWYO/KMVLEZPXlhqQOEsIo2JyUXYxKVmNSKHwYECF9ZoQgjJkiY3DZ1zXr9QohBbe3tzhrqSY5k6qkyAzRuUQ/iQfUqEjF+Xt6p8eqwX3gz5h1Ka2QWiNUUv4JPmU9h1h8Rz15SJM4fMb7zhb6QIOId+83dB2bzYrV5vbhohrHquu9/mEISQ4tjnnkIQinPl5ySggxcF/RJJHsD2l4KmmLGFlOc/QHZzyfR87mGSZPpei+3dHsbtjdvGVoPiI4j7c917cbtrfXfP97P2Q5mzCbaazzDC7SDtD1SS0mL924+EuUEmT6ITp0MimZzUqmVc7i/AwfQFyuWF2uaeqObQd5IagK+NH3nzCtCgqRMTiN9YqqmhO9w9uBYe+IdqBVfjQHdiiTkVcFRZWzabY0fce279j2lqZ3rFs/2kUJfvjhI2azCfP5hJOZocwEvXUEkaNMxnRS0LQ9/dBRlJqyNGS5wjYDnb9fvk5JTBxNCWJMThZKzNHiBMWMoTPcXG/46ss3/OpXXyAinJycjhxulcBPI8UoeE9mDI8enZFlkhgtw9ByfX3Jq1df0PcdZ2dj++JHP6Qoc/q+42Z1w3a75fW7N6zWV9yu3h/VlI6yaiECEu8itnc0vibXE5aLKSJWtPVDzeHfNn4HdOidasJ9jtGRs3fPIfrA99jv97RtR99ZlDbEsXzVtgPBgxKSqqowJkNIhSMgpWK/2yMEdG3LpJpTlhO6wR/J8F2XOGyrbX2kMBR5QZnnGGWwVjJYwWAlMVcgE0+p7/sHGdM4pVMvgIj2NcreYrefEYYtwTZkVUaZZ5xNJrwQS05NxdPCYwqLzkscCisDPQ65KLHKcOsjyyqSa4GYlsjcEInI0KXg1l/iZUE0Aj+cESWjSoQYZdrG8yoOnzDlFgft1GT+KxDBI3yP9DUqNg+OqaoqptMZgjCWZhKwKQAxBK6vr9jv97x9927MwEY1fJmEo2eTkrOTBc8fPeb5k8eczmfsN2vqzZqhrXn5wTNUplG5RotRxisIYtTpBhp7hVJCCG4sDYqv7fzTMpgbCcIQEVgnGayjd5G+f2jSCpCZJJYsiKOrR3rNGCLd0LPbbllVhpvLKYtZxawwXCynlJMJeVlSzCdJRcR7+rYjuGSL5duOwVr2mw3TokBPJjx6dJ6yZmPY7PfJUUQIDgawSiXjWykEjoRWvuPLRiQpAKZNWgrYaiyfutEf/nBk/dDTtMm+p65rQvAUhQLvkS6BEqSWiRZERMTUL3ow7lEg4kgoP/xeaY3OTNKvFAPO2gcoyPv8268DW9LXQWcMYnRpEQogjEpanDY5hzTNry8+h3uUtLam/vEhG77X7w7hQOtIn/34+Q6fK4ybKWRKqWKgzCXFSQXlElFq0GO6GwZE6MmkRYuQ+vzeU2/XvH31Gc+ePKXMBK6a0HUDnQ0MviB1ACIiJmi+FhGtBEbd5xInvdmyLCmKnGoyYQgRmWU01nO7a6mdIjORIo+8fPkEkyWu4L51aGOZTLLU30ccudQgknnu4JLAwbiJbPqGpu/pnMPGyBCgHVwq5WvNi4sFy+WU+XxKkYGIFj/sk3eoEClRGHqE8JRlRlVZyjJH7nxacx6sHIf/C1J4yBBMkEyRzBBktK3j6nrF9eUKYzSL+QIhDrKB/khDCiGMhgkz8swgRKRpajbbNevNCqUUi/mcF8+f88HzF5RVwWAts9mczXaLkArw7PdrWpvaIoJDzEmtEe9Tdovt0GVGPjXkWX60Bfs22eC3d5FokyfcZDLhQM59+/btcXFdLBbHZvWBHHuQSjLGUE2mXJxf8NHHH/HDH/6Q5XJJNZmwWCwoy4p+6NnWe27Xa37y058ghOCDDz6grGYURUmk4+TkhOl0StcNtN3A7bqhbrpk09O+o8hyjMlY1zaRp0MkCz2TQnCxnDOZTCiPJqXjjUcS3DW+Q+2+RNRf0F/+S/ywI9qWxXLKBxeP+EfPfsCTfKBUkVgPdI2gR9A7CUKSCcWTuaQoZ6hqiV5kYCS7+JzLZkP/xRfE5hoRBzIBUk1TGda1xF7jvKEakkRRlmkwpBKoGHmWB3khFwg+IvyAwjJhhzYb+rDnPo3C6Awp5Bj0YTKZ8o//0b/Drtmxb/bsdlsyJZmWBZPRNuXs7ITFYsJ0WvH80SNm1YRFNWNeVNhmz1/+9Cd89oufsdlt+cOPnqAySRz1+w5IRBlDylHEnXhyCClwmcwgRgRpykSSur8WgSSNZFHaE4aB7XbDz372E375xRdHmTspBR9/Z0lZpV60Uqm/k6zoU+FxlmUUSrFbXzJswAj40cvlWGoc2LctSmoqnZGVqReMkhR2ST8MuNAllKnuKUtPNZXks5KgHlO3S7wbF+0Q6dthvCHFKL4caNuB29t96r2MtB1jBIvlNLlajGXHru25fntXurQ+0I+qRC56gh1YvX7L9vNX1O+uMLMJ0yePOPvkI/KqTHM4PNTETN+mPprzHmcTglIgmE6mlNMJeVVS73Y0+z3rtk3KHWP5MYRUWjqoJyWNz7QZCwAhEKxl9+ottulwXc/k/JR26HHDQN3s2Df3Ny0p4B84q0ebtQjOWry3uKE7BlrGcqznLiOdVNVxSZY+IKRHyECwLdHWGGHJJgozqeh1jpcKaz2PFxkn6oSXsz+iOn1CNjFJvPz2hle/+AtefvicGDuG4YTL9ze0g+P85Y9B58hiQi4DufIUmcFkgmkmjiVsKQXnZ4+YlxlET5Hn6AjlbEIbBJf7AdF3VKVmTkYjp8Qebl99xdv3l5zMK/7RP/g+hVZoYWg6z75z7DvP1e2G1a4Z/Swtu8bT2R47OpTMplOKKmIKx6zKWUxLfvz9F0wnBUWV0bU1fR/YOWjaHpTgUUzC82VVcHF2wmzegypw5Z6Qr+Enr8aSmEAFjQ4ZCI2UCwQVuAvQM7Sc8uTpM16/ecPN6pKuT/KV1jlQBh/BOou1SRc0+OQw/+jigsdPHhNC5M///M95/eoVXdPyR3/0D/jed7/L3/nxjxMYisRvns+WlPmE07MLTk4WlEXGT37y32Bd6l+KETxmXSB6gcez3l3j5p7FpKIsMqpx3nybiui3DoJFUZDnOcA9lv9wJOlWVUUIYUSC+aNKCwKkSjJjQnqsa4kykE0KXnz3O1RllYxZ2wbdzFGTBZ++vkwgnPkZ08UZZZETwm1yEvYeJTuk1HgLkyLHWcdmk9BkziXtPEkidtpgRxJtgkDnBxPJcYToiDhyBJWoEWzZ+C2KAZPBx6cnvDxZ8qSqyKLGusjtfk89ODofQGZIqdE6I393RbmrKWctpqsQuabe7emGBi8GbMiwIkdkz/D6gmhOyFRGUJooJSEkzKe3CfgguMPlJMBOUl9XISEODZ4pe0RoiaF/EATdKALc1g3eWbRSvHjxDB89Pno2mw37pma1WjGZTMjznNl0Qp4rskwzyXOMUGRS0bct29War776itvbWzqbkHeZykfVlix91iiQIiG6Ui/h0BdTSfZKJbmzdL+F44bThdQBDIlwSIjJnsh7kmzc4aCE4Px8wmxRjg7lYzlUHliKglIYQmcZdg2OBJpaTKYgUz/LdD2T5Sknj56BTlnVEBzv31+z2WwZ6imFVlS5oagMJpeYTDCbFWR5MvMUUSAi2DIbObOp7+V9oCkygksi3N6nbFXrwHI+Sa4cRhNcZL9v+aufHkqXgau3r1AE5EFJPwS215e8/fM/4+YnP0ecnfL4Rz+geHzBLM+OoJUHSOdwp3abBMVH0ErwtE2d+myDpW9bhrbHW0csYgJ+KEmU4oH+5wGeEiNEkbhnfV3zqz/+Y7r3Vwy3K57+vT+AaYXX4ngu7o+jP+O4IRYjkjaJ4Mtjr/F+zkkcgTbe0zRN8qfTBqVTf9L5AXwPPmmGegYESbUIoYgBKgPFRDITJeSSKCJGah4tF/zexy9YznOUcFxdXfHmzWu6wXP2wQ9Smd5oikxRGEluQKu0UTuMGKHvHS0CvMMPjijSnHAh0vmkuKRzjchybJAI62l6ix0SYO3ssykn8wmzsuDdzYbNpuF23bLZ1XS9IyvK1BOOkb73uBiTaUAmMFqRG83JYsJiVpEVEp0LjEmVGKlyoqiIJE3RwUakMpSlwmQZRQnz+YRlG5nX/TFYCJKwhsIQvMFZlfrgMdLHmraxXL6/4vLqmuubG9q+Q8pkU6ez/D7UgoOqUNf1vH9/yeX7G3wIfPXlazarLXZwVJMKk2WEkFD+IUas97RNi7WWKANCeCaTRPvwfqwmBHEEKgZSeX2/bZgUM8qyAKCpG75tKvitg2BK/QsOhOBEkrdHmaQ8z48lGOccw4gSEyIB55SORCxtu6frG4IIXLx4TpYlmxBVN5jeYWaWcvEZwfkUBE/OqfKMoWuPN2eMyb5J+ORu4b3HiMB6t8e2LVpn+HG37rxjFChHaUVmDPcLGx6HwFEqzUy2IHeEsMcoySTL+e75BS9OzzmvpuzbpJLw5naXyOS9JSsmGJ2RZTkQKMuc+bym2E+RRcam62iaLS52KQiqJSJ/CdkS9JTMFARlCEphrSR66PGgUhA0YpRwi4yltQhKYrAUBKbsCb7F+YciAHZw9F1PW9djEBRcnD+iqAqyIme93dC0CZ78+PGjRFkwmhCTUPjQtITB4jvL7c2K29tbXn31FbfbDVEm6oUuDFKmLFIJkeyapBlJ8xzFb7U8BCtwUeAPC+vhvzEIIvyxrNK2HUIojCmPk1kAp+cTTk4nGKPHICgSUlJIlJBkVrO73XF5u0JERa4kk7LEmNRT0Cby+NkFH//e7yGrEhcC+5HqAQ7XzSgzQ1VmFFWGyRVGw3SSU+SJf6aQyCjxuR9Ff5MNlPeBKk9mq4LIYB2IgFKwmOdMqoLFpCK6yHqTHReNGCOXrz5HeksxW7A4PYHg2d5c8f4nf8mb/+pf4Z48weeGJ3/09yjnM6RSd1SHccixNAwJtKYzM8qlQde1CcDSJ+NbZwfgzjxZm9GeZgS/HHqJhxFJSjZd3fDpn/wJ+199Rv/6dcqiP3iOfPkilYm/FgTvBz85Ak1EBK8kwo/iG4SR83gXBONBTHlfp0yrTASlVAfriaEj+p7gehwDUQxkI08yBtBm5ABmhjYKhhjRaB6fLpmpj5guSlrrub255f27NwzWw8FAWytKI8mNpDAkMJV8GATbpke6QPQON1iEUoiQLLcGH+h8zyRW6KzEhtTH6p1nV+/YEKjKjCePTrk4W/Lmcs16s+fmtmbT9EQEk9k8wcKCpx9G30ypKOWIAs015+dT5vMKkwu0EcnTVBqUEQidUNPWJbf2osjI8iRokuUwnVUpCG5a7kdBLTUSTfCaoVUjHS7QdQ0xBopc8+7dO25ub4kuYrRiGAaK6o6zerehgbZtefP6Ha9fv8P7wOtXb9hstjjrkpGuVgz2rjzf9kmr2NoBpT3e95SlOkp2Bp9Aggev0JAuG/WuwZ04qioJJjT1/ltEtTR+J2BMnucjKV0fCbAhBNq2pe+6EUUpsNYhpOLZBx/x7v07rm+uWe1qmm6grlvev7/m9PyCq6sVP/y7P+b5yw8p53P2w8DtdsN+v0sHKSJFVTJdzFCFYeh6+rZjvakRynBymnYS3nsGZzGTgrmztDaw3dVcXd2Ozg+Spk9w5eDdEdkGYJzkRCv+4IVksdPYlebdLwznsznPLs75wcuPWJRTEIatt7zvGv709WfY0dmiKqZondT7qy7HGE2Z5Sij8UQ+vXzN7eaG6/U1TfZ9RHlOcf5dgi4RMkPrCUIZUBphk4u59Q5ne3CRMBLKlZCYEWEpBEkYm4gRFhssiK+pXASJ6x272xv6eoP3jmavGIYCZXI6l7g4s+WM2XyWKAsRbFD44JIbttQIFOvNjnfvr/jy81e0wZFNKkw1p5qfMVsuOOg0akEK0gI8Kuk+OocREWK6oW0Qd6otiORWXR5I/RERNW0riSHjyZPvkRWP+Rd/+rPRYQBm04LptCCEgNYpa/LOkmuDEYpmXdPvW9qmx6mCGB2v3u04WxZMpxnnpyfMZgVR2TQn25abmyv2u0skNS8/PKcsSsqiSLDyELHOIrwnWottO/woIZXQkgGHx43K+Kikrj+fz+CQicWAKSRCeQbXQQAfh+MsDN7x83/1z3izWBLNjB///X/MyckZk+UZ1Q++R4UlnJ4w+egpuVGImLhc9+1ihBBpgQiRaTXju9/5Lv/0f/1P+ezLT1lvV/SxQ4qk8avF6EiO5vGjJ1xcXPDi+XOKosRbN5oUP0SZqpgsq6QAebZENudI6eDRkjCvcBzmzq+70Kc+fncMhgnhl/wVh2EgegfBYfRY6ZB3G7/ofdKCzXOkiDhvGfqG4Bts37DdbAlhD1jOjUUqC9Fi+x58h48tIo8UWiOdpFrMeLJ4SpxWtDYQ+xkL/ZwQYJkllxghA1oLDvgfcSDzjyPGwNu318zK5GrjLORKMasKprmiMpEYLdM8cjrLyESkNIbixXM2tyuafc2f/dUXfPXmhtPlhPdvr6ibnt2+YzqbUpYFZaVx1mJtQssKmbK/R2cLqkJTZHByUjCd5cwXBYvlgpOTE96+e0vbOFzv6AdLDIKm9UxmJYuTBZv1FjdKXSoZ0Q/NMQh4dk3LzfWWeteh1JTziwm/+OUveP/uHdFZ2j55aA5tixTJmSfhCcSRKhFS9GSz2fL69VvqtkcKQdd2OJscQJRM2IGh75PakLVsm5btdkPT7NjXb/Bhjw97pPRIIWnbAe0F2ki0NrS9pdkPSYKtT5/j6YtTtPnrVH/uxu9Alk8Hdx+tlWXZMfr70aw2SjBZxnQ25+LpBxSTCXlV8e7NKwbrqOuOrhkIPvLTv/gL8jIHKfjkB78HjL6EYizh1Fus7YlxSjmbgVBJjFVIlFHM5nO8DwhrKaoKnEJ7yzxLpdu+G6i3a5wPWJ/4d4fa82HMM8lZEXladIjNls7VvJidcTGf82x+QiU1wgeavmW13XK92XDdbEdwhKAlJMFwa8htMnvNZLLW8THydnVJ3dY01hKLDGlKZDEBlYM0qXwoVXIY0CGVpMQBRRmSGj4CKSMHbPnRvYDkIYfQRB4uPoeSUt809E2N95YNEZ2VKFMQxvIXUrLbbrGmQyOSEgepLxRHK5q2aejqhtgPY28mJlcKk6WyTYhIIiqm8kUkjCULCVIToz1s8NOxCEkYhZJTOuQ5KhE5h7WewXqMLinKyQNKS5Ypsiwp4Cid+oCCBMbxPtB3o26nSILRUQiiTJuLvnfYaBHlFj27ptcRGwa87MinEpkl4eDMZKkvG5JurfQaVIbJkmfcATqfSo0JDu6cOoIBsn5UyB+5qj4GxKjoIqVI5yv72uUScmz2jzw1EcnnC05efkhQEOdzTp4+J8sSv/NQvvqmobRmOpvzyXc+IZsY1vsVu36bFnghyESGURmFKbk4e8xivkyke3Wv9Hxc8+8EtoUQqMxw/tGHTCYFw+Nzlh98gKgm7GMcN67f/JkOG5+DAHw89CCdS5WA4CH6lM1qdUSCx1EYXMSIc2mhbLsGQupDOieICVWG8wEZHSH0DJ0lOoeMnkyTOMjj2iKSxxVaSarcYBYTYhRkaqxQhHFejqLccPj3/vEkT8nBeja7hkmMGKWYFoaTWcGMgtN5QZWltkWmJHpa4fohiXf0nn3dI4REZSUFhkjOfFaNnoSpERsCTKYlSmnyLGM2LZmUhjKLzGcl01nJbD5hOptQTSums2lCGzcNg3U4F+kHOzqaJIH7YfDJGcb65MV575q7GPB9z+16j+sNWWZTm8knn0XfD4QQMSajb5s7LMC4eUlLTxzlHpPTS1GUqbxNiiMJGXwARqX2x2AtfT8klHq9Y79fc3NzCbJFyNTqUSNBWCCT+bkXiJg8Y6ezBLKUUpHnmrxQf/PAmFSJiMcd3cFE8wDPrkdOCGMqf5KX/OE/+Ee8evOKL7/6gv/Hf/wf09Y1ru8ojGE9rPgX/5//L13Xsl6vePHyAxRQ5oY8k3R1w/W7Vzx+cs5kNuH85BnBQ9t0STEhLzl/8jTByYHpyQm6b3HB8vLlR9zerFFC87Of7rG2Y3AeYzJMpo83qgBezDSfzCKfFLe82b5iWL3nHz7/HueLCWeLEjN4uqZmVXd8/u4drzYb3u1vUo1XKOjro05n6n9FDo68MUaaaEfRDkmWTVDFFFVUCJ2azwKTPLmAzAhUTHYnzmWjnFtyvFdxLBuOSu5K5ShK8BURT4jFw+sVEyS82W8TorNvub26RmcVOq/IF7NU4oqB3c0KIyW5TLqDUkuqxTRNIOfZrdd0my2FT5l+HhWZ0OQmJy/LdLwhoJzDjX2hwbrjhLUuJK5mDHdwfq1T5idgcA3O2kSuHZLsXtN0TOapXHw3BwV5rqhKnUA1h6zCK4bG0zaOej8abJoSBCgt0646eDY7x+3mlvlux85umX+4ACMxC8/ZYpoWNxsRBz0Xl0jAWhdMY5563v2Q0LwCHHeas0eOnY90+z7pKoYwAk8OuphpzgXvMV7dBQwhmZw9Y7FcjITkjDw3zB4/Jp9VPP/9H4IpKbKSspiOnFGO9ISHmp9pMVosFpycLnn68gl1t+P95h2JyB/RZBRZyXyyZDE9wegcHCk4cK87F1OZ+XCMaImeVXz/3/3HxH4gWkt19pS2brC/+hWZDBj1sET7ddeKA7LPjyIWvU08XhEDNiTEoxkdawiJfxZ8+mpsS9d3rPfrxJ91AyEYEBVCJIHpSI8dPM2uw9tE1F7kgkkhQKZs3tkeU1YIJJMywxRzlFAEHXFB4EiKMX68x6WMBPFQ4cdkGYPz3K63fPXumpOTGVWhuTiZgj+lnE0pck1ZRHIxUCrFbDaFUThdSE3XByIDL19+iFIKN/jkRiE8Q787XtvpszP0qEN6cT6jKgxVBouTKZNZxfzxKdVkQlVNuIiPMCZjtdrS9pau7dnXLU3b0/WWfgjsm473Vytab+gHfwwUEeidY79vefX2ikkxYSpKrHMYkzGdTqGaJFqcHWj3ew4yfQfXmkgSe7DeIaRkOpvx5MlTJlWF7Xvy3KTs2Y8brnE+dH1P07bU9Z71+or1+orXr75CZ568ACXyxMk9iHJIiRvS7yel4XQx5dGjCzKTk+US8+2dlH4XdGhH27bH3W7f92w2G6bTKfP5nJOTJc4H2q4nF4ainDBbnvJxWXB6dso//y/+c/q2oWtaRIzHXcPPf/ZzbtYrHj15ysWTZ5ycP2ZaFmB7bN/S7rbUmw2T6QIBTGZTzh8/QsmE6qumE6RWtH2HNgYjMoqi4uLCkGU5bbNnv11jTCLPB39nlikE/N7TgU8Wa+KbnzO1gTxbkhUzqjyVnb5aXXJbt3y52vKr1YrbrqMjqRcIfEp9/fglki6i8gElDgamKT0WaIpsismmGF1gZeJJxdHHLi0VFikimRKYsRQXgzgiY8IBghwDEyPIZWSqBb52yHBfuDiiVCL44y24Ade2vL+6YbY8Z7oU5FWBj4Gm64gmpw+Rm90u7cYVLF88STqWMbJf3RKbhmfVjNZHtCrIRVJ7kWMfcdTUxQ6Otu94c3VDkecURc4skym4xiRz5kNg6DtCTKa4g20TP3CwNK1js2vp+p6pFJj8Xg83xlHRI924SUsSgo/sdg27VY3rEuAjRJf6xkIiZDqPHmg7R1jt8cIxOZtSzHPyojx6ngmTsg9CRJXmuMFx3iWrmyw7ijC7A6dYHD5HUrrxBhASIc0xEMa0diJE6q2Z9qFw8emjp5yfnaGNYXZyhtIZ608/5/YnP2f75RvUowtOP/yQD37/h5h5McKA0vDe03UdB8/Bg9i9lAK8JFclj5dPU98tRpQwqb9MRhwSslXey/y8T4CRL7/4knfvLgkxMJvP+OiTl8ynU67/6lPcZkvY7zn5/h6vk75jMakoJxOE2B3vsQPX70CWT2vHcKT62CEFKkFSfxHEUb0nzf96t0MiMEqx229ouobb7QoRAjJCFhWZqVBKcHO7wTpB15P4s8EgqQh7R8OeSjlCuyLUK+bZAqk1PkqciygR0NLgosRKsDIjSkUQKjl7yPuraiqR75uWr65uyD5/xfl2zstnZ1RVwcsPnvB7P/4hq9tr3rz6ku36FtcVKOlxtkcSWc4XFGXBdFpxfvEILRN/c14KjPJ4W9HZjt45TDk5GkDbrqZpJVU5R2UZWVlSlolmBpJqMqPtLNaBtR7nA3mRMVjL7WrDerVntWrY7wf2NrKv7YPjGnrPftdzdbkjf5qQ6U3bMJ3NKIocFQV12yZRfO/ITLJpUypJ3oURM9IPPVmR0PrnZ+cYrRn6nkdPniC1pu06vE3+gV3X0w9Jd3a337Ld3bDeXHF9uULptPG9eJSntTaAswGiYjpdcnJ6znJ5ymK+4PTkhA8+eIaS7xmGh8pFv2186yDoRxLk8XSNdIjDBFcJuoVQgaLMqaYzyskUnWli9EfFhYOhZQwJOrherejtwC9++jO8j0ymC4yS5EaB89i+p2sahm5AKkmWZUznsxQgiCij0cGk7Gj0NWSEBE+nU+bLGUSLGPbHNP3+OKsGTrIG0WzSwp5XqEwTCXTW8W6z4Wpf89Vqw1Vds3cWT+IREe/UFFOqnHoHR3Fe5GjRpEjo1BKjC6TQSJL8090uLI7loLum7wH5eHB5t97jYyrFyAwUHZKe6DuCeyh/dLBf8i6VhYK1dHVNWc0QY6nzUI4KQoIP1NsdBItUkJ1OkaNg+tA1RGuZqQwdA1IaNCKVrqS4KzsE6PuB3b7mzdt3zGczFrMZk7NZEl+KBwktP3rKWXxwDH2HHRHHTWtp2p7BucTvFA+bFgdlEyLHjCiVEdPrmkyjRdJ/ZJSSkjKhs2KMBCEYesd+0zLUgczEsQeVFukkECQRMgU8OToMWCuPIgsHGbDdbo8QCa13QLkJl0rBQvjkbxdTdn9Uvhlri0rdQe6FEOTFhLKaJeucPJU82/Wa1ZevufnZr9D7Fl2WDPaTBGFXdx6Jh03pwfKL8dqEMRPXMkMbnfRW48FVJJURxbjJisfLmDi/282WLz//gp//4pe44Dl/dMF8MSOXmvWbdwzXN4TVBjWfoxZT5LSgqiZMJ1Pu16ESbyx5ByLSz12XDFLDSBZPPeWYnOaJR03eBMJLc6XrE4WqaWv2dY0izQ2l8yQxGCT7uqW3gd5GZPAj+Fhi6wETd7jME7uW2FkyC1pIIpoQ7Khek/ibPqaethTpK8kq3i2VMcJgB9q+Z9d2XN6ucTEwnxZUmWRalTx+fI53He9kUkNSkJR/nIUYji43ZVndKxEnoEmZSUw1oRs0vbfIvBgdYRQb3zDuABMYR2lMliGVJkaQSiNlmgNKa7QPCCkS/cd76rqjbXu6LlD3lrq1x4phjOBtoG8d9a4nPErOL/2QROzzvEALiQ+R3g6UZZn4k+KuDh5hlHEc9Y+NweQ5WkpCDMnTNc9xI5/QjQYJBxulrkuqUTF6ok80CG8j/jS1n+KIQDe6YDo94WR5ztn5OWWRMZ1VlGVB7xNe5duUQuF3KYdy74bNc6SQR8kmay37uk7lPal59PgpJ2cXnFw84vWrL3h/dU3b9XjnUvnLJn6JVAoZkubgP/9//qdcvbtM1zd45pOKKpthu5bby0uWp48pp1OKScmJPqdrO3brTfpQSjKZz1JZMQq22z3WDfR9w2IxI9OwftciD/22w7EAZ2rFmXJ44VDzGTZGNu2eXduwaWr+5a++YN31bJxjICReIYwGnHfnRCmVdD8BRPIyE0KhBEhVEplTlOfofAkx9TzCiDyIURCDRMaIxGGkpdTJuLPSis4Fehe47Xtcb+k7ixY1ki27+pfsdzuaes0Di5rocENHs90wNDW+75DekytNlRepNxEDiiRhFrzH9V2S/grQdTVBjDv5tkEOA1MgVxlKFaixT5N8F1NwEs5zeXnDV2/e8l/8v/45z54944MXz1n+/R9jFPi+ZbB35pmHBbLr2rQTHAa2jWe764+ahtqoB80v13t8n8qqSV9TINHMphWFyTgpp+AjrncMfepFZkqSZYk8u9pv6dqB3cpx+0VPdy3ReYvRoDWUhWQ2LZhPK06K+WgmndxMYPTSDNBZz7/65b9OeqOnJzy/eIrSmnW+42q/Zdu27Lr9MQAj1bio28OW6cFItOlUHhdREYNnu9vy5uqSV69fYaJFPF7wwtaUvoSxknAI/m3bUhQp884yfRSyMIcynkjdvSRa/TU0BJAYswly7gfP7c01P/nLv+Rf/st/yeAcz15+xPNnz5nqgl/+mz+lffMGe33NJ/OMxUcf8ujJj3j54SdEC3/xV5/ix9Knsz1uSEpJqeQ3cHP1nma/p+97JkUiNhfj9Tkclxt7yCrLGUJkUzdc3tzQ9R2D7ZnkRZI0lJLeBurOcXm7Sj0uoRNqMES87RlW7wgicjrXlBoqbcisIVNZAlaFQAyBsg8E0kLrRXJZQGmElql9waG0G7i6uWbfW5oY+fLylpt9jXUDL59doPNUWUFn5NWcbrNDDCG5ow/Jq1JAQq9by6e/+jSpDg0D331xwqPTCZ989JghZAzeMxAweUZZVUwmBm89MUqiNEShqappcr/oHd4FrA1oXXB+cZ6Q+m5gtdnQ1C1KFNSNp249b292vFvtOabtEYbW0uw6Njc1w3OLLRx1vQef1r3CGAZ76AuaZGE2IohCPDjZpz65kKkn3vYteZZhvaPtWlzwx6BpbbLJa/tkqXS7uqHMc8pHj/C14vZmze1qzdDFkZ8uWS4fcXJyxief/IBqUpHlhi+/+hlttybPLcLcMgz/FhRj8jynLIuRFpFKfYvFHO/TTrRpOkxeMF2ecPHoKafnF0kLrt6zurnm6ZPHLCYTfD+w2+2SokxdJyh0jGw3K7787DOyIueDDz9kPptQFkkpxvapCe6dxfkMHxI4oSxLBKkc5LxnaAdcb1OvkgghYJTESoG1YxP9Aaw80t9e0nQ1fX2DNwYvJL3v2DZbrjZbhhFNatToYRhjulHGLOloZSMPnSQxcttGklwIRJkRzQLMjCBLnGNcJAIy9kgBmkghd2Sip9INE20xIqARZCg6FD0RESxuGJDdhhDW7NbvafqefribzDFEPvv0U3azku12Qxy5m0KkrCYzhmKR0IueiBaSoe24VQr8naN7FMkstx96Qt/RdxAUiHBwMb+T0oK06Lx7d8VXX72may03NysEsPv+R1SFITqbdqTO0fVDIoY7R9umJn432CRdNVi6oae3PSrGBzs6PfYEtFK4mDYiyoi0YJWaKjdMyxmniwuC07jBsb29xvkBO/SURYGzga6x2NbSI7B9oJMBk0mWs8cs5qdcnJ8yrapE+DdydEEHKTXXtxt2uzWr2x15Zih0Tj3Zp2xwiJR6gqgSZchHhxcenZX4EGiaSJABJdS9RTWy3mySlRKSwSdPRHm6IP/4BdPoqM7OmD59jMnLVOI9Ukzu+m3AcT7ed0M/8AWT9KJIXmzO0/d9MqkepcEOguzpznjYxxOkRU0UhurDZ8hpjntyTvXyA8qLR5isZLE8YXly8gAdE1xP8H3qGWtB9JbV9SWvvvyK25tbqiInMxlFllGUBToz5GWZCPox0A0DWZ5T5DkhakxWURQVk8KQa02V5/TW0/WWcjpLgDVStu+9B2HQIXFj+yESHFgJ/ftrtMnQxqBGQI4a3pNKNxIhAtPJhOVyyUGw/m7VgLof6FxIlYUQoB94c71isJab1RavM3KtyIopJmrwlu22SwEXuHhySlWWVFXJRZiDCAjpePFowazKCJnADxEfBGVZEGJgt2sJHpA6mf4KRmEDgVKGPNN0wSNFhlYFUrYEPJfXtwyDww2BqpjgfKAfPK0Liet8b/jocS5ZGm23aU4iF0nrVymyPKe3FutSW0mIQ/XtUKUJHG3eYpoKUgqm05LgDMvFFClS5UIrkSggfcduv2e337Kvt5yfPWY5n1CpJb+Mn3F9vUJqw2Qy5eLRgqdPXjBfLDk5WeLjQNfvuL79in7ImEw7pnPLMHy9Mvabx7cKgoLkn5XnB0hbWvjKssRaR9dbhr5F6py8mLA8OWdxcgoEmqZmu77lyaPHuOWSYB2Xl1fsdju6PqlqxJDEVt+9fc3gHcvlgkmZk+UZXV/j7ZB2lK5A+0QcRUryIoeYnMDVmM3Yfkhw6AhizHREjCMPJQEqDhSJGKFZ3bDvGtp6QygNQUm6wbHrdqz3mxGeLDBCgPf4mBrnh5tBjlZLQowCwMjjApfKdCB0jjBzopniZYF1JBWU6DF0SOkxylOKDaVsmKktU9mghRvtVApkzCiiwXtHb3vobvBuTb27oQuewXZ3x0XkzauvsIsJdV2jYyprjLkAUggmVYXUOqHsfUSGVBb0PqmEOOsSUjUGrHV4ZxkcSJWNysdjKTccSNkps725XXN5dUOIsNslt4m6bhAUSBJ/1NkUDPthwA6Wtu3GIOhorKDtBvpRjEGPx3OYiVIotFCY0UorEJE6caSUEBgtWJzP+PijT9BixtAOfPnpL9jv19T7LUWR0bUJ5Wp7O9pmOYLw5EVGUU5ZzM85P38y2kElODaH8neQuMs9m03LetVQFYZ5WdHsarLM44UiN2kRskPDEAeGOIw74UAne9RYZjuMGCP7fY1ApnlKoJpPmT4+IX/xhErCYr5kcn6RSMmMhmrAwVbo6GE4Br8RM3MHmDnuJdL/nXc0bYNUIlVwgsTHROfgaxuPNL9JLYc8Y/rBM7LTOb7rmb54TjlboHXGZDJlOp3ey3JHNSGfMnIpIkTHbnPLV19+zqsvX1HmOZlOUlez+YysKCjns1QuG8UDiqKkqiq00mRaYfIMoxXaKLLCJJOfAGU1GTdmIFWPcz5p1zqbVHGCZwgRC+xut0iVythlZtBKEXa348ItyTMDEWaz2WhVdXc+YkyVgN6n8nqIMdlIrXc0dcv1zQYb4PnjCz58/pSsVPiuZbfZpLUk05xcLJkUBVVRkGcKkwvyieT8ZIpRgt1mk8TigyfLJzRNw3a7Js8Tt04Zc8z2D/eF0JIYe2KUSGGQyhAZuF1vCV5AVGRa4FziLPY+WdXdXa3kr+qCw7oh2dqNfNMiy0avwgQsdM5y4JLKUSowjBn1sV3BwYJPMZuWyBjZnS4xStD3HUZLYvQjInTPfix3F0XO2ekZU73g6vImAbS0YTKd8fzZSx4/es5kMiMvDLt9S9dv2O0vCdGw3UdMUeH933RPUCT3ZW1S32+zXdG1Lc5BWcwoyzm+DInYHAq6Adabmquf/xmvP/s57eaGP/j9H4y6ipGbm1v2+x3v37/nyy+/4uryiu1uz3azYV/XLJanNE3P82cvUsPYOzbX70GCLgpCFETvCH2H7Xv2uz1/+qd/SnQOHQWfvHyJEKCcZ3t1xW63wvY7uvyhQWYk8vNPv2JbVAhvGWKDi0PSGHUBR2Q6iWQx0gaPjiI10jFjDR9CSAEhwXZNWjDiMEpAgYsVOj/HnH6ELU7xsqRuB3AtKvZUZsPE1EzEntx9RaF2TMItWdwjgqftBNbPsX6KsUuKPhC8ZX/7mug2DP0VXfT0zh4XOCLUdc2iyCgnU/abLUNviSbjZrul/vwzJttbdJahM4MIEKwlSpCZAaHwPhBc2mDoSYlyYCrF2ZMXLC4ec/78CWo2TUeuFH20rPd7rAuYrOIHP/y7XF29ZbtdcX27YhhKikywrxuGwY6E2ASaaJuOuu3Y7hpu9k0Khu3AerdJou3x7oo5n0SyQ3SQJdqBF4lbGYIntA5ZVjyyPR88/YiF0GgjefvmC+SVoLi6YuqT6ssQeoaux4aBvCop8orv//DvcXHxiJPlSfKxJBBxdPWWrmu4uXzPf/OnP+Ev/+pn3N6sOV3M6Lukyu9yj9IGhoEoJBUVJubo6Bj2HT4eeGjigWajEII8y3DO8urNa8g1USuWPMIUM/L5wPT8jHw+I4rkOCLuPfc+OjSEr2l/ftP38dC3ScCHoR94c3XJeruh63s++vDjtMjdu1MEkcIYltM5/+Df+R/gbHKJny+XeOvZr1Zstltut9sHrRgtOOpu5loyjN+vb1d89eVXSRtVJAF2VCpvBiUTRUeAzvNUdjMZSo6uH2USXC9yzenJnPnylNnihItHTyiKgqoqKYryKNN2ULi6Xa3oupauaemcPYpvDI4kot22KZg5h9Ia5/xI6Nas1+sH59JGcOOZYSSIDyHiOkvdOZqffsarV+/45S8/44PH5+RaoZzn9GxJMZsyOz3B9wOb/Y6TecnZo1N+9AefUOTJUsj2gc2qZrNp+OKzL3n9+jW/+uWv+PiTl5ydnXBxcT5u2rJE1Wp76rrjzeurkSTvWCyXlLMJVzc39IOj7wfq/Q1dH9jse6wfq1rHuZRs8ZQBlUXevP6Cq8v3nFyfczarOJ1NeDHLiN2epq6T3rIsHsy9frAjdiRtrKdFwUfPnvLxy2dMJyV//8c/ZLPZsNvteHt5w5v3V3z6+StuNxuarma/2/Lll1/QN3s+fvExZaUpCk1ZFiyWy+QGYgqkgKa9YrX5ktvNGz74aM58VvH08QlVMaVef73h8JvHtwqCMTIqxAzjyUqajakMmfQOlUp9iLZt2dc1gxt48/o1TdOglaYYRZxDjEynE6QU9H3PdrvD2oGm7dJrWUfTNPRdh1ap7IUPtLsaoW6xPiKzPKEE+479bsd6teKzT3+VbJCUwtsWKRPH6+r6HX3X4OzAwa343r3NrSvQboJC4IMihjZJOgkP0qPw6BDRIuCFGrlucaQ1jDw1oRDCoESRgDHOE0QgCEnUp4jJOXr2iCAzXBxlhlyHiC2KPTKswK2I8T3oBiFrYuwTJN9LrLeEYNFiIBeOKBv6sMP7hoAlheuv+7glcefJdJaI/ULhbMAD3dBjt5sEKspMEmIeyxhHQINNPm4SxePnL1DnnomVPHr+IfOzc/JJRVCS4DwhQj8MrLdbEIqymnH26BH90NL3LU3TIvDYTLBvOobBjWjj5Fy/2zXUTct237BqOpxPvn3b7QZtzL1MMOmRKp2Me6MURCUSr1FEgoj4GOjcwLbZMwSPznLK2ZxqNqftGpZnpwihcUNkGCzWOzo3EJSmHBIoZ7+rUUIlagMesHTNjq6p2d1csb65Yb1ajYIJSdS3zHKKokhBOXhC9GhZJGJ6DEn8YPSjPFr0HO+vSNu2aK3HgJgcW4Z+7B9mGktgGKsARWaOcnJfF74+ZITfhAq4v4grKRPaT2mCdw+kEA8uAA81QMeyv1YUWXH0CSyKki404728ZbPd3pVmGUUdRuV/KeJR9BlScBIhEEWAIBMAgsgQA270PDRFEppGSPDJeis3iiwTZFoxm5XMFktmswVnF9eUZcV0OmE6maQsryiQI0k/xogxOXpmKOPBmzAeofrOZPRdkgSUQh4pMd55+q+hDQMJeHTImkfzrNRPJGWKsu6IPkkclsZQKg0mow8RKyFaC84yKZ+P9kAJYYxM7ZW8yJm45JcXo0gmAz7gQkTqxC+OUdIPfqRCJHHqrh1wIWCQKCkoywofEgjNDpLee9re03Xpet8NkUryUqGVpmk63BCYlB29sLS+pb2esl/XrG7WVEWe+Iwk8J4fWwZ+FEIQtkfbntJ1FFqmvm+MVFWR0NvXYIdutMDbMQw9wQeGPgXsGP2ovWvGEm1yuqEMSBlZ797R9msiXZoHs4qiKJKUp/4ab/q3jN/JT7BpmpFUq8lMRt+1eOfouzbBzCPs9htWt7cIJfj0V5+Ty7SbUqNpYgyeokgapIvFnEePzpFScHu7ou0iLqQF8qBLGnSGcIF6vU0n4P0ls5MTYozYoefy/Xuurq74qz//MzKtyIzm889+MfY5FE2zQ0o4P5mNKiP3ylAIrjjDyzMyaZDuFhX3FFoQY4uMHWoYCCKgiCgRE2k2+DHwSYJPZG2tcjI9gRhwocOjCMIQpy+Qiw8xyxe0IsfZiOtbtK+RscbEWwTv8VxieY0pHDFLN5IPgjZIXEyAnkwFlOpRaouPO0JsiMICDiG/dpMGUCpjcbrERYnIWnzbp8nkB4ZdmxBmWqGFRAvJxOSpFwQwJGWg3GR8/KPfp9QZ0sOT5x8wWSzZB0vvHa4bQKT58e76GqEMi8UJz198QNs3tG3Ntt4zWIlRkab3WOvp+4HtZk+9b7m5WVO3HfumpUnW7OR5xvXNdSKlH/q4QpCXOXmZJ1K/Ygx8o8+dBC8FjR243qx42rVoU1DM5lSLE6x3PPUBrS4TOKZpk0xT1+GFxOQF799fMzQ99WqDVh4hPBKL7Vv6tmH9/is21+/Yrm7RWYlAoKViOZ8zqQqCd3R9g3UOqTO80LgoaeotNoiEkZEyLcx3E5HNek01mZAXBbN5EntvmiYFA6Np7EDdt9TtnklVJgpOvAuCd0ErHvm6vykYAmitmU6nSCEZ4l3AE0KMtI7wIBCKZG+C0PIonxhCxChNT6Tpaq5ubri6vr73LonaYFQKKJKkUJLn+YgWT719eXDicA7vHZ0dsMETYkT7RNa2zjM0I3GegJSeg3JSPuoaz+ZzirJkNp0lJ/Oq4vz8gvl8xnQ65eT0JLmrzBeYLPmKBpKoRAyBaJODR13XZHky1W3bhLrtvmY+6+MhgzogvAWR1KdLGqKBurN0fYL+lyZjWU3YDx6TGeynX6JlpDSSD54/wQ2RdtficoFSAiU1Rmsmk4Qe1SZjMpuPffqQ0KAoXBA0bVLiWm923KxusNaRZznep6C2mC8QciAKO5ZBHbvOst83tF87LoQa+4s5626NFIHgHP12R733bMzA1abn7XXLxfkZRisi6XiT20miSFhnkX2D7iRFuyKLDiUSqlSIgNaSGBxNs+fm6j3buhnRo4rgIm5IkpdCprWg61o2mw3v3r7j/HyB1oH315+B6JDKslieMZtMKIr8Lnn6luNbB8HBDjRNw36/HxXhNbPZBCkNShm6dgAJpqxSZigNH374MXGoET6d6N1+z/X11agLKZjNpjx+/ISTk1OMMVxeXvH23ft0ICbB02ME5zzr9YrXb97w6t0bsjJNUB88l++vqOs92+2W73z3O3zw4Uvm81kyjqwbzCaD4MgywzDYcYcRD/cobvkhfvocVIUOW3RskP010a4Q9hYVvsLbLukVjru/4G2a6cTxBh5S/1HkRKHxZonTM7xekJ38AaK4wNoS72pwlsw1FHFDFmtCd8Pg1/iwo6wMPmQMIUOqjKANsaqIviB6g44eXMDHbuTLRaRP7tOau6ZFBJq6pe0HUJrHH75EakNQGhGSRFVtE0rLBo8aBaF1lPixvyc95MZQZQV5WRClwAroTitEZahvRtNd7xm8Y7vb8e76GmRBWVRonTObLTg9O2ewLd4FFJ51PTqArDZsN3uauqVpkti6jwKls9R70hplzNh+FIfLNfriJYkwqRP4SHiPjBIRBVFF7DBwdXVF91FPnClmJxfkxYTHTz5gf3vNT8Vf8frLS4gJQJVpiR8cu9WKv/qzP2E5KVlUBYupoiwMs3nFfLmkyEoG+5hHT9fc1D0hRKazkjwXRN+k7BmBFMksVeg9WufkumDSV9gmsNmuUaU5ZjqHcSCJZ1pTFiXTyYzpcsF539INPSJGqqJgnleJ/hE56nze//LxSNh5GP7iN/2CseKhuHj6jNNHj1O1xIOS6gElCpK12Xa3xXb9kSupjaFvOzjYf/nw4I2OWe8IykHIo9CGNgYxAjNCTJlEOIDNxmMIMY69d0lRmJHaEYkk6a3BW/p9A7ua65sVWmuyLEt6tqOqlTFZcpIvCoxJf8+rIlEITEaR5+TGMJ9MMUqjlUblB8H9AikF79+/f5BJH9ztDwFwhIWPWVvqFSbiR6QZpc8Qlp4erRzd0DCvMtSs4OZmTZULFhOBzlLSm7iZp2T5BKlyJrMlj4VCqoSJ+MWnn7NcnjCdzjDlhLodkt+lSALgpkxk/hg9WT4l1HuavudXX75hu+/Z1D15nlMVd2IUMUaa/UBVzPnRD/8Of/fHc/Ks4HS5xO1uCd0ehho1bCnyyGQyIcuyxDVWCtMn4YP1vmG3r5lOSgodGdaXtPsdZDlCKrK8RKqMPC/xLrC6WTF4R15knC4v+Ht/9+/z8oMPCL5hOnlPkRvKImc6KVieVax2r+mHLZ29ZnkyYbFcUlQGacDGlNl68W+BJ3goJwzDgNZJHuyO++eAlEZnmQaRbuqTkzNso7BtaoTWdc12uyOEMKrNKIxJr31xfp4g1d5zenrKbDZLgqkkrlDdNFzdXPHqq88ToGO8QXbbHc45iiJnOp2xPDmlrCY461CmQIhIcAO59EgpkpLJ3VERVUnQU1ATIhofJyAKEAXJY37FgMShGGJqwnscCA0iaUgGUkz0kaQ0kc/x5gJvTtD5OVEkTUaGDul7jK+RYYeINc7XSD8G5pgTosKGJKcWREYUSbc0eYbXhOASx87FJD4RAHkA5RxnM03bst/XrLd7ZsqQm5ysrDAyYkSkiNNRHi0mj7s4lmOEICBQQ8BIRa4NXbB4AV5BLyMxOuouCWxH6+i8Zb/fs6v3TKsyaXoqTTaWCIdmn/RNw8B217Jve27XyWy5bwecAw5OAyZL0nNZkon6OpcgQqJljJyzg9gzjL22cafc9R1tV9MPHVIZimpGluW4rkOZhDCO44sdwEKCyNDWDCowSMegNUoUOGtQWmOygmoWmS4WzBcLvLVjSUgS3IC3KcPKjCbmGbosQWcEmeMuV/TW0/eBspAHTZrxmOKRh3vQ19RaM5tOyfNsROIFMq3Js0RPOm4LxF1Z/v68PoTBeO833zTieG/nxdjbiZF215DOxgEZmt4hjGLhTrgxCKZM7tBmKPKcsrwTPB8vyvh19/2BWK3VPXm0MYv9euIaD833+yAfxgATU6kwhEhwnjiCuKx19/qk47UVqXedKkSSrCxQJnHsyrwgz3JO5nOKLGkkC6WSsL+SzGZTNpvtgyB4QJCOuFuOOq6HQ4iC0eyApKAX6ZyHwaF1xAdAqrTpG2lG3iUhjDiWvoVqyb3C+eQCUU2mRBK1aLevMVmFNgXWx+MXUiMIKWOMkeBhcIGms+zqlvWuYd8MtL1ntlggxxLm4chiAJMZlqclp8vnlOWUk+kc153g+5Z+e4Od7AmzLRfnZ6kHO63I8zLxip0ltx7rAvPMUKikrbuvW5ypyYvEefTjnNFKU+Q5vvNoqZiUFY8uHvHs6XNWt2/Ix56w0UkwP2Jp+g1dvyHPA3muqaoieSaqJI03oiK/YSf4zePbu0gUyUWi7/ujKoVS0LQNTdNxMn9GXk2oFlOEUWSZ4cn5E+pVxvY28tMvP089g82G8/Pz8WZJZbS+73n0+DEnp6d8+NFHnD7+gJPT08RBDIF93/Fudc3bq3e8fvcad7CFAOazGfPZlE8++YSPXn7E48cvWO9a8spwcvac4ewCNzQM9TXBu1+zesGmrxAkrZgQxIxgPkBwC+KKXdhhQ4OVnoYSHxP3D21AjWapvge7p3UDIisQJz/CFS/x+owYC7RtMc012m1QscOEPdgtPnQ0bofGk8kcmBKiYLCpJxeFIooMETQyCJzvaNqG201N16fPHGOSGY3xIYT78uqa6B3vry45ubhgMl9w8ewFy2nJfFIwP1lQlCWT2SRNMCEgkPpsAO2AGyy26/n09Ve03hLLjLDfIJs9q8u3DPuGoW4ZguP6ds319TXFszOUysizgjwvKYqK7crRtQ1dt+f15S37pmO/bxLcOwqyvDpK8eVFjjaassw4OT0hxnueecDgHNal7KHe17jgkEajsxylNTYmSybrWy5vv0JpwcX50xQ8TMm6btk2DW3f4IPDh9TTns8KFrOCR2dzTmYTFtMKIQU6ywhqgqnOKcqKme44fbzjokmecVMjqIzEtjuE15TzKcuTJ0znJ0wuzhmiZN973v7Jz7herRmiZLqoyLV6ELh2+z1ISTaKWEsEi9mMgwC3vHcOIIWB47Nl4k0mJ42Dnurh1cWDZOUukBxf6FjRO9gwpR7avR7iKIogRzGKTBsO7TCtDeiIkopnz56NXOD/8vjaUWqiNIDHC02UGpPnZGPfM0pP9MmF3PkREDNmhwm57MfqSxzlCFOm62NCBkchUdqgVVJ0UfLgZZo2Gd47nPN453BNeyzxSqPTpmksxyopKU2GVqkfNniHHYW//+iP/jC5bNwLgkZpRHQoUjk+iruTLO7wRwQS5zH4SBwGgtTkQjKbzpktJpyezzi/OOfsbMFifkI3NAxuwPrAzarGh471tk292GqKMgV26Ni+v6ZuB6SxnHiBDRIbJMqUiXtrA1JkuBB59e6Sr95c8dXba263Db0LuCA4PT9Lfpw///l4bAJjNGY0Iv/w43MWsxOWkxMWy1OKoqRv9qkv6RP+wlrLZrcDIQkh0rUtddPSdi2TqSZ0Df3tLV+9X6Fua5YnJ4nvFyPWRZYnp/zoR7/Pp5//EqUUpydLnj55zPPnTyF0zGdzyqokMxneWy6vv2RdvwPZ8/z8gpOTGYvp9FhiT2hUnfjF33L8TmT5VJIojuTIGCF6xib5gLMtQ6uYGkUWBUN9i4yOSZmzmE4p84yLs1Nm8xlCSPZNTVmmRmaMkJclk8WcclYhlWC1XmOdBylp6g1KRU5OZngnExpTKj788DlnZ2d89zvfA5mzuV3z5Vdfkec5p6dLlvOSItcIl5ObGUVeHcXAIZJpR24sSg+phCEkQRmEVggzISxeQPQok5MxIaDRERidH7QCQoewO0L3GqEystMTMj3BSQ1hjXZ7crdBD3vwA9F1RN1BcAivUEIn0mlVIjJBMJFIAqZoKdCjzJUdMrQt0fM5hUvgC0RLkIHoBsTq+nijSqVxPrJeb9h3Aya75v3VmkluqHIzwtFziqpAaXWn3i/SshmtTdfVeVbbNTZ4MBplMgSSerUlWEcYlV32uy3dvmEYHD6kxTHLcowpuN3U7HYb6v2WbZ3oEJGkUSqFoiiT+0ZmTEIha0mmJZlWhHgfyBTpOkvbJQWLJA05urlb8CHibERK0Ebihpa22bLdXDOppkiZbF+yzPDo8XlSxK8d0Vk0Ck3acZaTKfPTM/qRLdA4wdXNHiVbrm8vaZs90yrn9OKUUkQqHDqT6NxQnZwwOXvOZHHG9OIJt5s9u+0NplxwKkuWJye0w552c3nvsCJdPyDrhqbr+ezTX7Fer1itb0c1Do4ZohAps5Ei9Y6Sh59nv6/RJu2qE0iNuxIk9wLg+Nz0y3t8viOyODJ0PVfX13jvKYqCQEQpyW634/LyktwYok+csINx8/XlJW/fvuH95eWDjMmTNDiTfssoQ6YNyfIoJsu1UabuqLQk5VHizR+z/LTBj+OxxFFtyY5wfEkKPjEkU97DZvc+h/IAkEklV3nkzcrjOUnAljhShEJImfl3vvs9qqriP/tn/yxpfwpBkRm8FBBs8g+MAX/IBsVx68ERbCIgijAimwMhWE6mGq1mnJ7OmC+mCJnOkw2C3iluVjvWm5bb2z2zWcXjx6epLRMD0/kCpKbrHberLSF4smLCYCOD69nv97RdajX88ou3XN/uWO/axLcrMvKy4vT8FOvvKmNCQFYJylJiMsV69Zrd+op3Mmc6O6EoJ5QmOeaYLMcrk8BG2UgVE4rJZMnST5NEpbXI5SnZ84/I8nyspmSjwEPgrKl58vQJn3zyCf/Zfw51vSfPDbv9muvrtwmhLCLKSOp2Tx8EViqmc8NkUvHo/IKqrCizkiIvUQeBesLDythfM36ncqhSKpUKhLzbbY4lwbSj7rGdgEmJ8IKuHtAiJLX2sqAqC7I8x2QG5x11W2Myg9ZmVDnIqKYTtFHEGNju9rhh9Jlqa7QSLJdzok8wfiEMT58+4+LRBY+fPOP6es36Zs3N5XvKqsBoz3L+OHHKtKGsqgRhPy4MgvncsDgxaC0ZhMQJhVMaGUtEEET5DI8k5lOcnILQ6MAYBBVaCURowW3x+5HEf3KGVVOcKPB+gwmO3DlUH4gOglPgC4gBEatRWV0xm5coLVA6wfIFAiNLMlWipaHtO0QuCJlMvQc8yI4oA/u+RXz26fF6JXh4pOtabN0SheR201BoRaEVk0mFzgymMMgxCBqjEoKPeLScigGGoU+oWCkRoyST7YdjWqKUPCJ6vQuEmFwulNRIqdjtWzbbmt2+ZnBj30eqZOujFHluyEz6UuOCm8i58ghjP4zBOobBoxQ4GwkhgSOIJD+3ISTvShUZ+p6+3bPf3RKDR+uMvu+QSjCfT9jeaKKThEFQZpIiVxRFTjmZUs0WuMHTdQO7bY3zW2JwXF2+xhHJjeT0dEEWI3ro0UWOLguKkwvyxWOy2SmqvMBtI/v2BpNPMFnF+ekF7y874vCwd5b4tj0xRl6/fsVqdcvt6vYY+NTopyYP6kQyuSBAagvYYUgyVWMZ7z6FIqE7H9IpHkhdxXsM0zEAXL6/pB+G1IMliX6vVivevnmdNh1jZhZDpO86bm9vuL29ZrPZPLheUcixoiGO30ulYSTwe+8QowtJ6rGnzVjqD/LgOA6A2iBAjh59BwvCdD+n7w+SincfIv1NCkGQEjlyW4UcVasOJdOxJH/oYyqRfPqePX3KZDq9WwuB3GiiEsiYweAYQkjnJN77QIfMepRAFMnsAiHT/SVEJMsUZZWhjUrSgT5iA3QWblY1b9+t2O1aXIgsT2cIZVOwKgq8E/TWsd3vxxaVJqBxbmC/H7hZ12x2DW/er9jXHXU3oM2Esiw5OTthOp/S9d2DqqE2kawQKC3ZXt8wdIGhC+TlnDyfcLqYUVWTZMSdJTS0UImsr5SiKCdkwiRLtL0gzwtmiyWTKrmURMCNhslFlSygHj264N/86X+DlGktqZstt+schQIRkUrQug4nAspKqvKUk+WM+Ww+cnIzMpXaKEpKrB++1h747eNbk+WLomAymeC9x4/E60k1YTqdMQwDNzc3WNvibAfRYrROnlUkbnVRJPNVkxlWqxVN19K2NUVeYbIcMXK9VpsVWZ4MVYma3XrDdrPm9vI9Fxdn/OB7HyPlFO8F3eA4OZ2SZYbXr9/w1ZeveP/+Eq2TLqCMHd62BJmRmUQyDv7u5pBK8L/49/4x3//+72GMwo39sIBEpT0sbnDYGOmCQOeJe2R8IEpFFKO1TrQI3yLsP4QoQS0JMicKiRD7RLPAE1w8SowdRI4PxOeDXVIkEMeSXiKueoyu0DIj+ibtmEPi56Q72yJk5N3lO/6Tf/JPsXWNIFkOFblBxYLr1Ya2t4i2p1EKIyWrlUaoNIEZFYCUFkgB6t4CknbeiQYglaLIq1QCG3vEMUSstWy3e/a7JqEAhSCMSjN1s+fm9pa63o3mvinoGpORGX0HZFAKo5IJ7mEhkpGj8PJh2FHtIsZIU3uc8zR7R1nlZJmhrjtibNmxpxIzXBOx7V9Rlcn3cXN9Q71ZE7xjWmim2ZTq2QlPnj1mcbrg6fMPqWZLimrGmy9e8eXb9/zrP/7XlEYjY6CvV1w8PuP80Sk+nmKFwirD8w9/j+nynOnjDxAywwXFl2+3vHp1zWefvWE2P6WuN/zxv/4X+Nhxu9ncr64lNKlVxOj513/yx+z2e6y13PX3GIElIMegdhChP/55fMyh5PmbSp+HJ8R7PTjB/d9DjEn6KlFn4Ivdns8/+zx9hjAKL8RUBVI6bR4ePbpAyPtcXJHK1FkBY79Tu4A2SSUm04YoBVJENJDLRNjPfKRH4PxIQxjBUT4wKjaR7tKYRLSFEIggUNxVeGI8gG08BwRL0k4dgTZj0EsLp0qehVlOWRQJAW8SoKaqKi4ePU6dv3vn9WQ2pcoNmRHc7Frqrudmm6TgnLUHf5Hx3k793Pms4MMPX7JcLJAh8vy84vnFhO36PdtVoGsGZqdnBKF59XbDT37xhs+/fEcMkabvmcxzFkuN0oK2DwwDBC+oh3YMQoa2SZSj169W/Pyzr7habdg23TjXBLNZwWRS8sknHzE7meHX4dg7i0R611AJQ1kZrpqG68sdP//5Kzbbjr5zlJlOThBlxvnjJWVZMJlVlCM1oSgLhEzl6JPZI/KsoCgLFvMFWZ5h8gytDUIKVqtbmrpht9my3W1Sq00JXr/+nLq+4TsvfwAqIHKHH2qKqeHpi3OePj5lMZtSZdW40ZaIqBBxrBD+TiHwdyDLHxBdB2g0iCSaPd5ZVTUhEtE6I0Ro+4Hddpd4QkpSFQVZniF12vGEENJk02bk5DgSClvCiBKzg2O/27HfJlsRozV5XhB8WrQLkZGZ1KPb1jvy3PDo4oy8MJgslV3sYJMjgJB0XZek2u6tPrKMyIlHaQ8EfASBSZmIlGhvENbStQ1ZAVpppAWdJ75a2+3RMpJrzTCY5Gnnd0h9COR96g0ISdTjZIsQRhsh8KjRhsmFBICIMaBMuqB101DkacHAQqYyKlMwKxaja7hHSHBjf+MwyiJjPqsQk5xu5H5tm5phJCYrPfZElDwGQaFE0jrlGxZQkhdYVwSMydBKH2+euq7Z7Uf/Mu/w3o4BsGa32x29JhPlYtwMjf0XqSRaJgWYhEY1CS4fAwqBu6/UAaOBsiP4BKIKMRycq45BIv3NcXl1yW63Z7vbjUobBtskCbjoLSrPUDKjmJbIqoAsI2YGrwQDgbzQLE/mvPzoAwwSEQO+X3J+seTkdA4I2q5nv2sotzsGVdAXW5qmp2l7rt5f8+b1K778/HNMHhlcy7pZoQwM8Z64wXhcCY0ZKMpylMnaMwx29PC8QyCOMYHRXu1wi959Jx4GtbtIJ3hYJYoP/rkfTQ/Pl+Pfw4GGMmY690WutU+Zadu1d6VWUoa53+/ZbbfJjFupJMcHzOYLnjx5Qr/NkMGho8cHjwuB1vl03N4lBRuZWh9epI0nQjC40arKJ3/AtADeLX5h3KAN3h4zXXHvuIRWSK1QRYaSI//wIGpdpF62NoY8zxMyvm0e0Eim0wmzqqAsDKro2Xc9USrquqHr+jGrT5P34mTBcjnjww8f8f3v/R5np2eEbqDS6UuIZDTbDw72PYMfeP3uhsubDbebBiUE5bbm+nZDVi4oRYZSGfN5hZIZUTqsT9Qj60PKDpuObdOzbwcGN5aDRfLcTNJtKeNPG627IaJIJWU3EIXDR0dTd+y2NW3TsxeJq2uMZlc3FGXGZJo8OJVOgtkpa5c8ebzF6HR+01o/8pLHsvS+3tO1Hc2+ZrVaYW1qrXifbK6KoiAvDXkpoMyZzXIWiwllbjAjIEuKwzb93n9Hruy3G9+yHDo2TI3BWnusnx/r6VIym89TeUIZmrah7XveX92QGUWeafzcUxHRo5BqiAliG6Mk+KTMr6RMu8MocD4wdAP1dsd+t0uL5dg3al2AKMm1Gsmaga7eM59OefzonKIsGNxA09T0vcUODjOW7NbrzYPsYlADViauiR1VJIKoQOYInSG1wIWeergmr+YoMlyQ5EqS5SoRxDPFJCtpvKeLPdthTyZNknJzSRxZirRjiSLyEBwfMWNZsOvbIyeuykocgev9hqkQlBrwHbNMMZsVzJfLFBjHse/6u8AloCoLlosZVW7YNzX90PP++hYRk6ltAgaMpR+V0HtiLLcJEkT+8PkOEyrGSFEkb7FEk0nBc7Xe0rQd/ZDOoXU9XdcmIYP1Gu8dUgoyk1HlJUabJGjNocylyJShMBmzqgISoV8ijyCvw7A+pGBrxwUSEndTkpr8UhLwDN7y7v1bhJCUVYnROUobMmEolKJSClMmsWkzn+CNoZeCXgpi9FjXU5aGx49OmRYF0cYkERcdy+WEqsq5unzLrm54e32NPrliaiOlFVxeXbFar7l8/Y73797y+tUrTCmQJiAzT6FyrHAPZkHyHUw8q5OTEybTCSDYjujnu+rlXeA6lIp/6/0e7z3iGzY24xX+tWj6YC99/w3u6pNAPIBTQUDfd3evN86XzXpFnpmjgHYMyaH+5PSUD1++pF+XSN+jfOKwOh9ohqRr6rxLmq1KI5QmZiXIVE7t+z5tHEIcd/+KI0torFL4EOhtfwQG3e+rCq2QRqGKHCXVkZRd5CkTnExnCcUqJW3XcHNzx38UQjCfz1jMJlRlTjELzLqeiMRoQ62bMUA7iIFnj8558cET/vCPfo+/8+O/y8X5Bf1mT71+S7N5S7/vsRGsj3S7nn3r+PyrS95dbVhtG4yWZBvN++s1J2fTscpTcHZ6QTWZ0vR7dnXNsN1iQ3K/2dYd+7an6W0ysB4vlA1hFDgZ6FtJ3/bH+3tc1Yk+bdhQSTCk6y1d09PU3eiCkSpYt+t9KudOsrH9cpgMqS//4cebVNq24fgeUqnj3PHBY4fkI0pUGG0SLS5IjM6pJhXVJKecSqq8YjYrOFlOKTKDkolXKuUIVBznWzIF//oa+9vHt1SMibx79x4pGa0/7jzLnE8Tdt80aJMznSzISArnZ48CRa7Ic4XA42LgZnPL4B1KpUgPihgEzkaKMmc2n2Cto+8dwjWUeYEtSsyjC87OzlieLKkqydBbmv0e2zVoJXl2cY4pC3SeEwgIaQi+SpclBiA5XuR59iDLmeYFs2qKNnKEJ6dMUIiEGjPGUBYFVZ4IuVIqvIMsSwCE+XSRbp6qRJiSylvKcj6CTMSooydGHpg59mKstQn1J8Xo0eixOJRRlKPFCkKQMSM3BUZneGPJTUaeTUZn84cB4niTIphWJdNJSa4VL549ZT5fkBVTmqal7Xq2uz1d16Ud67i7PiDlDuCL+9f/sJYqlUyEM5MdKQlt2+K8x7kwWt1s2W4rtts1+/0Ok+UUsmBSlZQ6H7VV4ziP5MhXyplUFdMiT/572+TG7dzXjlAKdKbICk2WaaSE4JIdy6ZusSGmLNuIIwhKaYmPHk9M/ejgaJ3FKU3dWF5v1vhokVIw/+xzssyQG8OzxQw1CiIU5YyyrHjx7GPatmHX7nlzfcNmu2XdbFn9xZ8k/zmVMdgO7x0aTZ5HXn58ysBAFAElI8E5OhceXDvvkx3ZfD7j449fEmPgv/6v/mustbRt82uZefyWN/nD3lj8tdcZH/Tw+99USzpWZcd+2ghEyPOc+WJGVVVJKWTcmYcQ+NnPfsbV1WWqbqjUIxYoVJnz6OUH+PMZIliEHwjBJV5wHInzh42BkEipkbo8ltq8S0LvPoQj1eWg4BKJBBeSY7m3yRE9JPGHY7aokxWRys2xh5jAMoyfPz0nRsG+duzq7YNMcL5cpg35YIlSkeUFj84vmFRT2m5gWdd0bU3f7vmD3/8+H3/8nB//+Ls8f3ZOVZS8X99g+4b9bk+mckyWUVQTfvXFFW8v1/zi07es65am69FK8jh/zPOX3+PlJ59QlRnNdocYS6AfvvyYwVmaruFf/cmfU/c9b69vqPuOKOHR40dUxYQ8L3BuQGWK12/fUJqCum4eXOBC5OiosHZAqoGs9Jw9rvDRJR6eU8mhwycqhZTJjzH4dO0IJK8dEfnqy/cQI7Z3R3UlIuO6IYGQ9F8zw2IxR+mEEJ9UC6aTU5RRVBPD6VmVvCqLVBE7AGC0UUgjECZtggNJ4CGt43/DQTDdGweB3tFFWJkxk1BHryulE7k0IkZ/K4ExyRIohuQZ1XXdKEfFsUQmtCIv/Ngb0gQXkSItEiEk/k9VTcjzLIneakX0ASVFQpEBeZHI9Wk7GEcgj8ZZBzEiCWhtEh/q3nGFkFQqkKOYtEj9BhEjxEAIKTMQx9qTGEEc2cjlSjvGIkvlgBAcRZZzIPweBG4F93eiMnG/QjjKbPngMdKMfbcEIRdCYkhlBCU1wXiU1qm/+Rt29YeRhL1Tb86YpL13crokL0uKpksLRl1TH7zdIkfE3IFXdbhI4bhTTD55Qgg6ZY+76nQs6XFd19I0e5qmpu0auq5LqE+dQFUafbSDTfMoHW+RF4m0nOc4Z9FKf2NlP8Z0g2mjkfoQrGNCKR8mv+SY5coDwGbsvXrhkDL1hjKpRvuqJPvmo0eYVBrTSrJUYLTGx1QeHbDMuxn7esdut2UzNOx9Tx8dXdunntVo4qskFGVCpEahiC4tqEokY+GvH1uWZUwmE05PTxMBvU/2Q2GkiNyJJf/Njd/4el8PhPe+v+PejRuYIqMsS+bz+VGQ4v7rN01NlqU2yoHALlVGEKCzDMkkOcsHRxxTOS3EUbotuZ+ke0bJchSnkEeqw+FxYeR9pmxg9MkMAeUzlB2OHMxDqTd58aWKiPepxyylPD4/Rp9eTyRZPufuy4sxLuKpYnVwVBEiVTuEUAityTNNn0kmVYkxmq7tWN3eUps9u92O/b6hrjvktGDw0HSOm9WOq9sN27qlG4Y0V6QgCoHSOYP1wMB+3xCjZBgc5WwyVr56bm7W3KzWtG2HUppJlrFcLscgWOL9ACIkJ4cuGaXfH1oalFBYQMqIzmC6yGg6k8quFpwH77jjuobRWHqMDwhBIHmLMgKtnBuvUTisM+IYU9TohSgOSUdZUU0mgEdrqMqMaqLIs4TeTvMo8QbT64Sxupei8GFT9G3Htw6Cs9mc2WyOFIJ8rJdLqcjyZOQZtSbLC6azKWVVpUl6FonBEsMAhOR+vU3yZ846hn4gzyryokTKPNWpfcS5ZPXinaOpa5qm5uLxU7IsxzuPEPpoCaRUOJKre+ewbYfQhkjiD+33LcFbMh1ZLOZkeZ4C2jj27ZZ1vUpmlEYfRXdlTIu+tkk5o+t7cl2Q6ZyT6TlVXlIWJcvZIr1QTJCau77DwzFenge/E/d/vLfLPPwYAabqCHa4v3D+dZc4xIBzlsENDC4gpOTJk8fj+fVMr665vLph13Y02xrrHIwLzP1eUrwXAA/lz3gAHMAxaB7qdberG4SCqsrZbNbU9Z6qqlK2nGVgPSImFFhmMoxJ5sdFnlMWOXluUDbdDDBqKd4bNrjRXWDM7sYILSQoLRHOpuB3kMeTkOcGaUyicux3EFJ5vzBgpGZRZtxuHZ3tybRgGHrqZqCbVlij6PBsd++JQvB+85qm62j7fqSYWjrjsd4SSQpCk0lBWeQslnOsH8tJ9ehuIA3Kx9H4lOM1Pz094dmzp7x8+SGffvYr3r17x7v37+i7/jgn/20Ewv+2I5UPNcvlkvl8ztOnT5Ov6H5/7BUDDMNDfcoQAox6sxDBVIhRU1Tqw80TRhPmJMEVQ0QEgcEcoe8HUFDwnsEOiSLECOhxPrUdwtjgjoo4KuwexuGt4tiLTZKKOj0/prL06FcFAbzvHxz/YJOLwuDS5v5gtKx00iut5nO8nRKGJPqxWW/4sz+/QY2KNPN8wn57S73dIkxJXVu+eH3Lr754z7vrFdu2ZxidNHJlkj5v3/PpZ19AcPRNkzibWU47WPZNzc3qlj/+kz/n+mZN03Qsz06Zzue8ePE8tQNkRl4Ygh/ouj3Xby/pmvZu7UFgVI6RCsuAVFCUkotnBSLrmewjfW+wLnH8+joku6cuolHJRBzwImnAJpUc0JlCZ4qDqECMI7/5KA5x0GSWlEXF6dkZ5+fnWN+gdWQxnzCdZcnWTGkKk6PHxCuKtBHycVQ3EvGBTdm3Gd8OHSoEpydnXJw/Sv0Jneryk6pKC3MEud+Owqtpl5GEgAvqraWpe1a3u8SlAW5vVng7kAVJkU3JC0XvHG03JAktbxm6lvXtFV3fAIGqrBAomqajqTcQEz8sASgieUh2Kd471qsNQmq0KZEmwxQFk0IwqWa4I+IujTKvKLMKFzyZzlLfYuQoJW3GdENmSqNE2smLGJMF6hGIEtMqPBZSvr7Lv9tYHybb4cSO339TCepQ577nY5ZwEWMv5u7lvnFIkxb+SECS4Obu8BSRgEyLpefR4x6Vbeh7ix/LSXCXJdzPOIUY4ecxjIoPh97wmNsJiXMD2+2GV2++YrA909mEsixG9KfGiEOmpcmyBLZKcOuMIs+o8gzvko1T1/e07mHj3mgzuqffP7cRk2myTKGDSkAKo5OSzgi5lzqBgKbZBOUiykX2Q4f0Aq0MYmrIo8aUBdIpMmcIIzy7LCuaJsnDbYYaHx1RB/I8Iw+KqsqS0ECICX0cA4HAvqnxMeJ80qyNiJHHpNDlwyD44sUHFEXBZ599xpdffsXq9pbg77Q7/1pniN8y7jZW8b9zEE0emhyzVEgBUSnF5eUl69Xqwbw8eEYe3jsR03XqwwmJGOkSCJlKmTGJlifDZUiXPykiHcpoh01BjEkmzjHO7ZgyOk/AxpB6TsERRCSqh4LlqFT+10rdZYCHbDKQXEpEojekMu7D9sButyfLDMF7uq5JiPkIkYGIoPcB7waCH5hPNJNpQTHNEyUyBGzd421HcAPNINjtBz7/8op3qx3rpmcIgTA2xJWS1PWeT3/1Kd//3odMpyWTakLTdAyD5ac//xWb7Y6b21u26z1KGD548YjFyZKiKpEkd/vgB5aLxyiVMykzun1L3fbjjZ0umhZZAs6pRFkKMTCfe4ScMltkWAvWBobBsd8O2M7T1X5UnRKIoDjIzvtwOIfJPzWMPN6kNgSuTzuMVI1Laj9N3SAl5IVi324IsSfPUm9Wy2RTZlQqiSaqaPpPyjHLlwIl9Aja/Hbj2wtotx27fY0dBsIIQBiGBJIRUbDZb0AkwiMx8eWqsmS72rBbb7i63BAFqExxfX2LH3pM0Ehd0LvAuu6o657dvkXEgO1btqsr9vWOGJNpbySJ3W53e6SQ5HkFeJQW2PFEOue5ul4hpCYrJmRFUkQRQSKCxA32bqEncn15w2QyZXB2JHSqo4GqiAmBGGNK5ROU2rAudkynU8qiOEAAjudJ/IY15td+fb/EFH/9ceJrP38d1HD8+1iGefv27RHwE4mst7uxKZ3UJ3wAG+WxdNQPjn6wKZyKZGGDT/Y1xJS1iq+ltYnAcWg7x2MASqXUBAVv2mbMPjsO3nZuLE1554naoKRKPEeZbhbRd0nH1CffQh98ErZuW/Zte3cOIjT7nizTdMYmsQCSoLJSqU/ggk8LnE7/Hs6VVIkSAiBcQFqPrXtEFBhlEOOiqGwin0fvuep2mMxgJiXrpk1uE2F8TwFZkR/BO5K0u/XOJycG54FmhOqP51iQehc+sK+7h624GGmahjevX3N9fcV+t3vg5vD14PW7BLP/NoHvr3vOoWzorGMYBtqmZbfdstvu7gXuwO3NiPqDI8/RSI2SaR4IbTjIqQUCqbPjjsfu7FhxIIlKC5m4YAcUoHM+CXX4IYlgxwOyfMCPIJBjz4+R1hPD0UxYy0QIOmSdh/58CENC4KoUKHf7+sGxr7e7JBEZI117oAZJfEjCNs1gU48zWN5dKaq6oGqToa2znv16AzGgRKSxgrpOprzbpqMdbBIJGINgjEkG8e37Sy4enRCAIs/Z1R3NqNCy2+2SMEbdoU2OyfJRZBvqpsH2Duciy5NpMhGIbkRzp/l8OK7bmz1ZI2iHmn3XMtiBdujxgwfPiMQdP9qIFP56jSseJeTu2l6MqPcYw0gTS5kgCKKPqeIRYaM1t9c3zGclq+0bds0t+7qhrUdOceYwanT5EHeauVLq415KiYz17f63Jgn3h/htk12Iu+XZjJD6+w+/nyU8zBzupTbxzv36+NjRXfk+cTeO5ZG7hz288b8uG/Xrn/X4tOPyPP7lLuMaR4Kcp5FQjoK7zt3Xx9f1F8WvHft/H0aMka67U4TXOvUfH+AivuE5d9fnmx7xuw/x9ZPNN5+ru2xY/PrPh7kwfsb7Qs5KyfEp92puD17gG9/lm0e8d2W/4aH3M3bir5+d3/Tqdxsjwde/vXvriL/H/zhkt/edG/77Uvr8beM+d+6bgnWyh3rwjH8rn+N4l/6NnbL7LyR+bR5KKe899Nff9MGzxa/fF/ftpg5zPvyWTD212lJL4vBSR9T2ODmP5//YPuFuCo6PPbYuiGNV50B7ScOYQ150J6DwTXP/sG7c/9uvbdy/aTxYj7626h6Q6SN24tDfi4fK190J+23vgIDRGPzuesX4m5uE3zoI/u342/G342/H346/Hf//OH5bEPzW5VBjNAdD2v/uG7tvBnjc30H/poD/tf3/vdc4ZIq/KfU57K4TEvAwijx/QDL/mxh/bSnpd3qxb/+ew3DXvFejCPLv/D6/7TnfkNF842scxt/Apj9GHuzAfz2z+N1fb/yOA6n2roqRZt6B+nG3a/5Nh/LNvz3uocM9o9tjP1iMKGYeICnVaKj74NUf/CyO/8h72df9BOj4+e+S6QeX9dgEGH8fYyLci+NLP7yHDq8FjAIZd8+9n4zfHWN60fuu9EVePACiHT/sbz593/jY+5/9fsXo/imK8ZC13K9wxONn/U235TdVKr6pMnWfX5xn5tfm4REd+bXXGN/lwee9/7ffXFX6bbnVX3fyfsPBfu3XIcYHzjpKqWPv9f59cT/Lv6u6PSgLjv+I4331tT/92ue/qwTKrz3oa6v8vQn36/HiNxxmCAx991secTe+VRCUQvC//d/8e7x49uSoT5jq+zGpB2iVGpFjDymOakvO3528lManQ7ivgXhI8ZXWtH3Prm4oc0OZaU5mJYPvkku3yjHKoKVJ/J1ROFgqnRrsUh3f6yB+65y718D3RJKg9P/+3/8/UjfJyfv/8h/+n/nOJ58k2sZh0Ygj1ySm3qCUCQF7KEWEEAnRjejEMWUftQxDiPTWMYRkrXRQMzk03FPZAyLp/VLqPvbYHoj+3nveYQIee5nptcPhNRGsb2/4D/79/x1d1yKl5H/2P/l3mc+nDwPycYF4GKi/TpZOVNivz9y71e/XSp73SiMPxrcoXQDHBjeHRUQctC4ldd3yn/yn/2+cS0iy/9X/8n/OYjFLiMHxMcboYykF7rzpjmWskJRlUrnJH13U3727ZL9v2Ky3EBMA4uJ0yZPHZzx7csHLD59TFDlFnnFnoZpskIQYpZrG344YIaKA3jn6YeCrN2+4vr7m3eUlu+0OiWBSTfn7f/iHSKX5P/wH/yestSit+Z/+k3/CfHmKyQuaQSGF4vw0R5DQc8rM0FlGlue8vFgyK3OmVcEQwMWI95FZlbOclCynFTZEbuqOwUdiFFRG0Q2edvDU/cC6cbxZdfz42ZxlqROlI5NoJVjvLEZLykLzaJEoK1++vqTeNzSd410rmWSKRSHp65rNds+nb95C6FnfXvJ//7/9X/HeU5UV/9F/+B/x6OJRsr4a5/ZBWi+BXMRxjoRxjt8tuhw3EDFEtps6KfTs96OebWSSJwNVJQS3t7fU9f7/R9uf7MqWpXl+2G91u7HmdLd3j4jMjGwiqwooliiJVMMiiihxQEDQC2gmCBoIegJBcw010CtIryByIIAUQYKgWGSylBWZzKzo3cPdb3caa3azWg2+te3Yue4R5UmVNmD3ntaO2d5rr6/7Nzzc33Ec9kx+4uFwxDQFZWAYljUAXSMGyMfjjmfPXrNabWrLU0juwzQy17+ltGIYBv7bv/hnlFJY9S3/1//T/47Li46QPK5ryaVwOMy0leY1+4ngPWGeBbVstNiLOeEpj9NRFHFSYrO+kHmn0iz5ntAABI0dgiTty76pUOjyGECatj3tFVOYBRCUUo3KZ4LkZIi+CpZntHH86uuP/B//L/93Us7YxvE/+Q/+XV599kM++/zHHI9HComuh3fvfs3xcIvTkc419G3DcXcHJeOcxTjxgN1eP+N+t+d+tyPGROMcm82KFLzspwWs63FNz7MXP+Dy5iXPXv2AfvscYzswHbksjhT+pKTUVaOFrutPCVxNyyjUvXDZ34Bf/vVP+T//b//Xj4bcv+f43rJp61XLdt2Dkjmas5audadMclEYySVXkWVNTCLTk3OuIrI1w9ZLYBHxbKU00+TZ7fa8f/eBy+0ac73l+uY1qbSUkiUI6hZnGoFEL5YopgoLa3sCsKQog3VRRoiUIjqc8jrPOXDw5s0bfvSjH1Wu0GlbrvzEdFp81tkKyxYV+lwiiXz6vZxE0T+lzOQDPouFiugdIqr7RVXkGSySSqcg+CSLfZwPLPqcpSyXXI5YL7pcY12dAx4r2vW6Z7tZPw12Z895fpzPoE7X6HyY8DR9/Y7M9XfIFH3Pku3pO+PJrFi+/JhFb7cbLi+21cOuihA0TwUQlvlNqUEw1iC4wN+FgD5WFSQj81Oq/U4Rek7wHq0VXddyfXWJs1VIgOqEjvxOFTEDJUCROQRudw8cx8DhuGcYB4L3UDJKGVQprPu+6uWenYRmg+63NH1LaWVDefVqWzc3TdYdTduzXm948+aGbd/Rda0kWxkSiqt1y7Ntz/V2QyyF9VHoMQDbRhOyIqTCOHs+7j3tu4HPXq1YNxYfM6tGFJiim+k7w9W25c1lKyohuheJqykQPniuV44XG8ew37FaP3AMgZQmyP6xKtCKz19/xus3b6rhr5yjLNmxXFUtKO/FmWapdJfKcln/KWfuVnvGSQB63sse0DuF1RIErbEc+h5rNI1TTLNFq4hymaLL6f4UycYGisPowsVmS7/anGTaRA/V4qyryZY62TuB7Ac/+OwV19drQprp1mtygf1ejGqdFbeSeRoZj0chdRuDcY6u6g/v9neEGEgxcnX57CQkkJLc/+d7jveiBrUo7kBBZ9l4tdJ0q3X1VcyM8yhmBmfdE6UUOXhynCFMshZLQemGw/jYPVKAcQGlA7lE+tUabRRtX9jOFxgXuVgpnAarQWGhCEpa2wbjGlZbS6Ah6U4CtLX0XUcKlpKFvtC2G7puw6vPX3Nx/YLrF89ptzdo24ERLmQICWUSpci6uLi4pG07+tWaBUiltCCuJQiqer/LlnG4u/vOruF3Hd8vCBYYDnse7j8SY2S1EsHUzl4x+xk/T6hKYPUhsOo3GOvIGHwQHs1SUQnBMVelENHoU8rwy198wT//6V/zX/xXf8HrNy/44x//kOfPt1gbMKpgCFgTsLo56fgtC0MpqdQE2fX4iCmRkwTAFKMotX+qlbdstpyraQiUfWkjy5cf0ZBii7KwYlRVQiiPoIbC4wMqfJ4TBFsodur0nGLfcr4UkaztsQl0+lgtr6O+VDGLOJOvWi5Zecy6z49z4MWTS7xk6FU1Jp/+3tOq8dPPf+9RyrnX6O/4mbMnPnsty3VZYPin79Vru5Coi9boE6Fanb2XReuyJio8vu9FtGEcR6ZpkoCKqPt8uL3jeDzw4cN7CorXr1/impb1eoUz9ZqfWqSPayYhXoe3Dw/8y5//nC9/+1v+8qc/ZZ49MSactlhtODQDt7f3rFab03svwPtDwjeBy1RQzrFuDJv1hhSEYrCbjzinae2WxorY+jgHYlGCPjVNFXbIxJwoaJw2Qg2hsHHQtA3WOVIMvF+J44cxhSEEvr5LrJ2iNYp3h5HrTUPXiqKOLpnWNXTO0feJ8vYrGu24XLXgFXMHlyuYZ8XePm1ZaTS6KLkW1TcwRU+KoaID5XWtVmu0Xkj4tR1XE8KUhOOnNVijRZjCGEpOkOV+zlWRxjqLbSzagNGJtZuJKhByIkyIvmaMWDTOWlZNh1GKkiKzH2sLWElQVRpXKVPnAxOFottesLrYEkpgvb0EZTCdr5WgKCId9juUu8e5asrsGjabahZuHH4WW7HN5av6M+a0d5VSarHhmOb55Lc5z7Oct5RYjMxXF1ekUggxkcY9OSXakk60jpQK4+GB2U+QvAhEaA21k/V43yX2D78hp5H9/YE3b/6Erl+RQ0ElQ+d6XjxfEcOAn3YUJlm9Wol9VfIch0JKAWsim/VWkukca9KjaFTDtlmz7i54tr6gtQ4TJtJwSzIWjGF/OHA8Drx//x5rxEi6tz/AljVJbzFWkK/W9KAtYMiYGgjFYceeZNz+1cf3V4xZrEKytBJW/UpuPu3QTjLyEhMlFOaY0TmKZUhtF/ogslTGagkkWiHa8TM5wy9+/gt+86tf8+6rb0h+pjWGr7/6yMWmoW0MRhUUHoVmGEXqZ4Hway2u1otkDojiRAiBeR7F2yrnyveanlZHOZGztE8fbWfOc4hMQQnx9uxvLnDfnDM5iR6iwLXzY/WWRcxZPN8k+En19jjzfIRtfxJoEGpGWZQQag/gzLKstjWpES897dH/norvW9d2aSNXfUcMGKW/83fKcg6+x7G8NNT55+Xp64Qzf8f63r6FOj5v3X77Z77rd3V1M1jI1cvvlRq4Fnk4xeJUsYgRaMZQ8HvPX/3sN3z57pYvvvnA9fUFXdex6fszrqI6USKGcWAcR96+f8fX37zl7v6eYRK7KaOFt+Rsw6pfs15t6PrV2XuEw3EHTUvSV+AjBz9hf7Nj3RlWrWW7ucS5lpigN5qVVQyhQsxrcmkrzWM/zIQMxwApZ4xSbLuGOSR8zFUkvbBtCiqPGK34h39wJcLlqfCr2z3hXsQsvqgcutkHrJb57NuHmdYanm0ctw9HjscZow0+Fib/FC6fczW0jZ55mogpEGNgf/uBYf9ADJnLZ895+cMf0tjaYeqa+hxaNGSUXLO2qdW30kyTJNfj8MAUJ3Ka8fORGI6g7tDqLYYdpLcUtNyLIaBSpiFzfJjp2i2vX3+Gs5ZSCg/7B1EhVRqnDLokGoN4mvIEachxf6RzhlAiKQs5+3j0jEZjrWHVd4R5JkwTpEw2kRIyhyRdsRID5IQuCT8eibOpybWsd6UUyRtGpZmmGWstXdex2+2E65zCiQphmkZEGbynhImSIyV5QlXKAYP34vBjKGSVqyaroajzClfx+aueogKHw1cMhx6tLnn+8hqlkhQVWc7xPB3QdX+f/CSmy8bS9A1tq2malugnSjFYZVk3K1rr2HYbtusN61XLWh/ReabMD8SsSCUz+In9Xip+fATjyLnl49f3aNOgVEvTrmjaFVfP3tCvL2n7Lc6uZJ8uUIo40nzf43sHwVQWQyAFugEtQtIFBFiiFYqEskWMaRE+iZStUKjSXGWRE1PoLETilArTPJGCx1AllFJgnmdCa6tjgajP5FxOFAet9ZmsFKcsSnrrMvcJwdcbMaG1YZ6fKj9IhZBre/JsUFu/u9RiVAMXiTyPgStXyaSc8ndUWY8loXrSyK6U+vL4lI87x1IenJWTtXVU6vMU9fhzy7lU/O7e93m78+mAWz35Xy9+amcB5lPo+99FteRp+Koff0dwziWfAqX+PZqo9dc/ifXlFGglsViyBHU6h6f3f3YuFk/M81bvAsSO1ST17uHAOHsm77nf7+m7js16JUo3dQYeQ2SexDFjnEY+fPzA3f09x0HkqLTSoB+7FevVmr7taZr2HGFAjiISsVQdpRRGn3DVoFopSyqao49MIUmbLOYqW1jtiLTCGc3kEzEh44hcwABKvBlLyuR6r2iFyMcZxc3WotH4WHBWoUsmp8hhFrNin8S6KMTEfko8HD23u4H3D0epqOfMbozsp/jkopcszxNDwM8TMQRSDuxvP7L78J7gMxrF5bNnRBeqCpSiKLn3Yg2iOXjm+UgIHu9H/Dzi54lxvCOGgRxHUhzxcSTnHZQ74IDWAyVqYgTNLJ51SvFwPAjHk0zJiUTGzzNGKTAGZxpUkfdMTlJ1nh0i/OyJRJQSzuc8zkSjRBhCQwozOQaKlhFKKopQRFUqJy/aYykS56neVxldxU7kHtSUohinSZKuHBmPe2LwlORxTU+Kga7fEFJkDh4dZ9knq4h9TBGtG1J1d9H6MR1U6mnCopTi+fWKkBR+msnxnhwLxrQ0TUCnKmSQPDH4k29liPEkwk8RmTcFRO/FPECLW4zB0iiDUwVLooQDqShKVoRZhA2GeWAajgQf6vihodAwDwGw5OJoqjenc7YWVJpWu9pBkPvS/B3Qc99PQBvFpC4Y9A25ZB7yBT602JhwVtE4mUlpoGtzFVMVgTmtNVYpSr26+qxMLUpJO0dlXr55TgoHmrTn6tk1L9+8YL12dJ3oaDZGrJDmeaTruhNPR0RuBZG2EGyXBaVUYbXqKaXgZ880zTx84iLx7d4lnDt1qrMtWZ1FrUKuDtGpDtNLDcLyFEZzmlnlJCoWFE49fZZZUpGs/HEEV0e9RYjDT7qc9cLmZYqmqET18nuD4PK8v+trzooQ7rpfnwJeCCJDpZR6gs6kPMHfPjmLj+fv6d942ur9dnU6zpPMN4zY2eglQLFsBo9PKA7ei52XZK+GWrlWvufyyFU1JFUlkWXOlIuWNoqyyC2g6+OsAqUwzTPTPHH/8HCa4S6yV9aIdJ9U+ItWpYgroEDp7smpUNrS9Ss+//wzbm6uJDk8OyudCVw5z+s+0q0tru1Yb9ciAqA0D2NkjJFDGFHase0aGmt4tu1Y92Lz1FrFTW+5rYT+mBVojbWa1onO5BwiYwgcp8gQMtd9z6ozNEax6USJ409fbGl04npV+HAfmEJmSIXb/czgIx+HxHG458tvZn777oOILWfP7eHI/YfDI++sIJuznxmHI/vdAzEGdCm8+/Vv+OaXv0AnQ54968sNAxHXNLy8eU7OEyl7Br9nHg5Mw57jdCCEmWk+MA87UpxIaRRpxuLJxUORDso83ZFTZH3ZcPgwcTx6ujbQWnEruX1/z+A997cbbCdm2XmecM7SGo3TUgHGacL7iTANT9ZGDJ4wT8QciLMn58xwHGkaAQoyH8gpYEvApCKJffDE6k0V4vE0p57DTE6ZEGas06cuRimaXBTH4YgxhqFtGcdR9tcc0FoslXLw1Z0mUfxITp4UR4bxSEqJttlUrVQLiwUaUnWeV4LWav7+T35ETJHt9pa7h3eEdEtIA22vWBlIMZPyzDx7unZFJIubAwW0CBH40TPPgeODp9EtunPMPpI16EMi7g9MrcJ1hWILxRXGMpFIRBKLfKM1jXi15gkYQFmMaYnDgTA2zOORzeUdm8uXvHr9xzTdlqZrAYX9FJH8e47vXQl+EzfEcInTiruDQw9VxqYpdC1sWoWzirYzJGUhZ1o9M+XCnApT2aBQuKJojMVqhdUJbwyZgn32hutMHQ57bNsTM8yqIZmeg7Z46/Bdx2CF4G7O5nkxZcmkc0TX6FCSZLuUQmhmpuAZdZbB6pOjBpXTp+db6VI5nHxaqlfZMnssFamaHwMYSwCUuJVry1apVOeBWdCk9fmWSrGABODqtP0YO84v6FPIt9ISL/UnnkPfHaq+fSglZrnWOrque5y5ZZGuSiU/0YM4x8ich6dTeFM8OY/n1dfpl07frjNLpU5i18vPl7OfOQ+cuYIkTgEtJXRK8sKUOf19ybxrolLyk78ZQ2QcZ0IIxJhq4lLO3kiGU3Fa6t+Vs5BiIelM0GLjslSmucKAM4VFAkhraptI45yh6xo22xXWakHvnb2m/fv3NMBF25BSoelmnIGLzZpNv+Jyu2YIhY9DolUSSIzuGUJizh5/KPhYPXC1zAlLVTGxQOsMqsi6SwmiUfRWs+0cndMMx0CJklTkUmXLQqZvFX1red1YLlrNfasZDw6VEjZrXF6RUoMqER8jO33ehpJz7UNkDoHRT2KBFCL348D9POHHxPzhLeGLntFMaF346itDSRMlB2L2pDAQ/cAcxGki5RmVI5QEBEpVminqMSEZ50ROhaKlqdq6TNPoE+jG1iS1NQqnqtj6qqNzjlXTopXhmCKHlE4SiudHMYqsiwhSJ1mXSlWQXA1gpQqIlQrKWxL3XLKgW5FkxZl6b9TPKaom6gL8UCpXJG3BWkXWGooR5ZWS8POANqBNEZ3m4iF7tIqgEuSZjEP6XotAPoTo8eGpMLjWhe2654/sK55Xla6mLxgrLdqsIbjMYCJaJXStpEu25KiYJtHKnadZCoM6ypGRUWYMHrIiJUWnlahUpUTWATQ4q+U+RhS+VDFo4zBNjzENtlnhoyIXS9ut2K63XF1c0nctzlms0ZT/fwXB+9xjyoaVNvhZhIuN0XQp05fCc63prQZniEWByjiVGUNiFwvH0mNQtMDWdjRGoUwkay3Ctutr+pRxqnD77i1FWaY5YpQl2Y4RR3QtUWfxFFMKpyswBkVImZID5EBTRIqnBIECl5LIumG2gUmnU9truVEfd/OlbbiAUs4nSbUdSTlt3gtCNdfgdwLFIMAW6WgsmZ08XaIGUNST1t5ZZwzUI3L002NRJi3qLEyrT+JkfZ5PZ42ffry0P8UM09L2/UlQePICnEghnrq4yylaFOD1pxvD8qrVWSA7+7/mI6cjV/NktHDzdO3jy997bOGeH7k8KlwsFX3MGZVFJHx530u1mM+uY0E2qxAj0zjhfaygGfmd0zJQ5UkAfGwj19esFtudR/1UcjklAao+jNGYairdVEfuvm+l5XUuDF4Kx7uPtEZx3PSEkGjmkcZkLlrodMPLreMYABVpVMQUaKyAsnxO3I9CO3BGsVq1YuVF7cIYAU5ppageyjgNndWsW1sTW0+KGaWRGbrO5CiO6I0zPNs0mJxpSmJ/acjeUKJhZXvpvpTEVx8PuMpPXc660JQCPgUm7xnniWmY2c8z+xg4jBPz/UfmrzVzM6CKp5mOkGdUSWgNJU+UNJ3MYJXKdE4Saa1T9ZNUoMQRIvjINMscXhm5jo0tdI0hJ0UIBaPlunTGVGseSVJWTcOqaQUNOsvIRhf4VtqsMplEKuIGc05vyllTiqlBugp7FYXSyzpK0mlR0q0qxtT1nqgxQ7AAipqwCyhI1pSqM3QrOpw5E8OELZJ0l+Lr2CjIeEolqZRZ9gzH8m588Pgwn9a53F+ZtrWs12sufE+MnnE6slD5ilJ0ttBaCfqpov5KVhSl8T4zz5FpDpjyeD/nUohFoMxayx5honQwSglkK+A0g5X9uUig18ZiVEvXbXFNR9ttGedMypp+dcHFVh5921amgIya7PePgd+fIvH6Wc9nn/VcrgqT1/hUSMXQtZpVZ/jhlfCMmsayHyW6P2sgjpqHwfDxo0CXtyvD5vmWduV4tdFMHg7Hmf/6L39O2j2gjhO//Nl7Gms4zJr/0T95w831FWNy5KJJxeCTODY0KmOVcGCCT7Qm0xq47hrSnBgPM8dJMq6mXzFqy/24Fv+85a2p2k48BcHFOqjIAmUJSLqiOyWjSdUcNOfHjVqrBa5b++1q2ZgVFEWxsugppWZxT+XisnxRvreUe+dHjUSFUgnI9e8rgSyfH08lmD5p9/K4sWutadqOtu/ZXF4KuCdGki4MxyOT9wzjXG2f1In4nGJ6BEudlYWuIvcWUWJKwVSOpTaGnNJJm7XUaNEsFjefRPLvAtGcf28JgjklQpFZ8eJYoJQ6bYDLIi5F9Dl3ux23d7eM40BYpJXq5rP44zWtwzlBHHvvCSGSoggHL9dF1yTMOaEKUddLv+pou44XL54RvWcaB0wpdF375Nyfv5dp95b3wwfuvvpb+vUNrluzvnzG5z/4Ia9fv8EPe2x3geqvBP1mFG8uG24uNjSN4+dvBxoLhsDdXlqhPmpiDBituB8nbKW+CDhNbM7WnUWTGcYdtw8BHzLHyWHXDnvZczxEgs+44vnim498uNth0wSmgHGsOlttzzKfP1szPPSP67bAOE8cx4HD8cjusKvn/8gQIsk6dumB3e1H3k4PdNuCyp64v6PvxHnEukLJEzkNFGw95+CuLM4alA5y7pGOTIwRPwzsD4oQFccxYShYpSm5xwfDMIrHZNdu2W6uSUl0X1tjuTRwaSJrl7hNmV2vycNT3mwphcPxPUZ3xDCdgF0xlJNptdUXde0lYp6hKGZfjYBTZBpHrDO4RoTaS420Pky1G1PQrkEZ9/TuLRW1XsTtJMVE00ilFXKWFm30zPPAkgIqVVBGXNiVXpMpzHPg4+0HPt6+Pz13zoW3H+7YD3tKTKzahpISx/1O7MGMYr1ds2oS9mXH7hCrm0yphrgtORkUFmsd67bF4MjFM84ZqxzdZkO7NXS9Qjl/mkSUqv08H0cKDqUcq9WKi4sXPHv+Oa9/9Gf06wv69SUhLvuuo2lXONeBaqmDJUDRGPWt7fN3Hd/PRQL44XbmD68GLtaRKShB8yTpf7fO8LJNNM7inKZJAktfKU+vWnpt6VyDcRrbaVxT6NrCzWXH/iD2R7v37xk+vifsPvL+w47WGa6279F+z6Wd0J3CF4OvcxdNwemEKjI8DhZao2h04aqJRJ2YSqK3wnkxNvDObcjmO1qh5bHCe6wZls/rBn9Ovk4SBCXQnM2uNI/uE2r53fp7KJQq4mWn6/fL2aJG7GJKOfvep1dhCZhLqUFdQKUSx5+8radBsJRvV1XLoZUg4vQyw1zazLVNubz+XBQlnvm4pVxnGeHUtlytwTqZLYl3nHlUQ1Hq9LHR5oTa/a7W7VJ5nn9neQ+nSrx+fD4LXvRTjTFYJzqjMnupPKpxZBxrW64ieSXfON/kHp97oWssFe4j7uYRRCW2QrpWQBnnxPdMEgGZo8hsGIIXDlQI6cl706rUyiBTykiOGT8U7t8VyryD6YFmdUmzueHq6opyccF82eMrV9dqRWM1ndP0vSOj8UlzOMo61bWHUIBhFv/KEAKdnXCq4GMhF7nevYNVI4T4Ic/EmNiVwsNxZD95tq5yZXORDTwXfIzkUtv+Z9fQx8i8WHoFL/PBaSTkRFaKYgwhBuJuYvYRoyIm+koZqEo1RVCwVmuUVljDKeEQsE9dBzESvSfngGvWKCdzW5VbIIG9QOMw2bK+1LSup7hOzFhTJqSCbgvrNnOzVgQyKs0VjHPeNiykMJKj0AGKNrKNZKn4JPvK0g6ntpdLefQerGLyWgtqNcQgay4l1JlAu1FU82IFOZNCqMFYMc2B6L3cQ6l6IubAPA5SeftZyPbGYC31XEkALrngvVSB5z6JpRSOQyCmQBhHptahSmYajuLQ4ixNH9HG0DYtzgWsU2gTT/ekMoW2sbSNZrvuUdlA0ISkMUrT9I6m07iudntUQRmFrh6pxjQY22NNz3b7kovLF1xdv2KzusC1K5kTKihFqmFjbFV2Kpygbapg/g6Kn9+7Hfrnlwf+/Nk9681IyA0xW0p0KMQSZWVnjGkFAdcmfIwwHtgkw0XRXHYOGkO7gt55Nm3h1eUVJkTG4rn/8gs+fv0VD+/f8jBG+tawaQJ2+shz/Yo3F4qQNT4BJdcgmAnJkIphTg6nM04nViqSXWZ2Ee9ncpqJ8cAX1n3HOLA8PnisPJ64EytqPSjgFtl0S1V+EX8slKqcPWmFJjihOEsNQtK1KVgrmczSP5M/L7+/tB5zOg9iy/Z1+pVTgFBF4DP6kx740uJ7fJtPW3vnX1/ml0sysCQEy7xQZl9LAlD9u2qiE2NiGB5pKEUZ2k783vreCJ2gaSgIgrZpBFnZNA0heEKQG3YJamp5Z78D3XWipeR8qgRjjCfU6vF4PBHowaFOMy6pEvb7vVS440jOiaW9e5oAVjDNIv11HnDPf/ZkLlsd1hvXoI0YfForXpveS1toHD1YR2gy0xSYxlilqh7Xl7WapjU0ncW5hNYThIm7bz5y95Xmt7+8pOu3bC6e8eM/+3vEV694thUrs75fAQIe27aGF9c92lrmrHl3q5nmyLrRFAw5K26HkWEK7IaZeQq0VpxgnGtoG0XXZK56zVWjuS2Byc/sh5m7w8DDGNi0DTEW5lBQJeN94P4wMIXyyby9MMVA6z1znQfO88QwHplTIiqNdo44eHGG4UjjMpdbSymVz1tqBWMtjbUYrdBWujTi6JGIsRCDtAVLCuTkWW9eUvQKtCPMmZxAdTfo1NC4htVWlK9S0xDzTMqB5BN6AxdrxcuXmjknynxgHDzD9Oi7B4USjuQgQS2VGgRTRCGgq4ULrbUixEQm4uOMRmZ5KSaMseQEMc+UIkGsreo3S7A3VhG1IqeMH2f6vgdgHgfmeSKXSAialAI+CDk/xkgKibbrcA1YJxvF0oZNKTPPEyFMMtM83VtwOHqGITDsH+isqILl5NmsO7q+pfGRrhfz9MZbXPRYJ1UcKdBYQ9c5us5xcbGlRJiPiSmBUZZ+3dGtC02bmXyqVaqm7Vc0Tcdme03fXdG1W66uPqPvL+hWF5h2A9oQQ0RpWxP22m7NGfTCEignU5zve3zvSrBbrehWW7R1uHKBKR2+NCir0E4xhEn4MdsV+4877u6P/Oq//UveDT23YU159SdcPLvi+fY5f9I4XlrDi3nicNyRd7fc375l93DLcDwSksEahQ+y2aqc0QVaLeRVS8SoQqM1Plti1gzRokpEl0zJssEYqyE6Ulb4WLgbOt4f/MkpXU5gqgS+fFrki8tFWqrAIif30ypE5l2GpW5RSm5Yo8SDMCtFqu1NOR5njQtUueS6AZel0pDvq/JYH6naGj0HpSQepYJAlHD+Vcen1Iic82ljWuYNIQqcfZ4npknI5MfjkRgl4J3cwbU98R2NkTmDUkJD0cpgjEMpgwz5i5hh9uIabbSuSj6fKOOUcmoDq/LdgXCxy+EUrDl5G57mdiCvN4lbucDDMzFm/Cwu1/DoTi5P/Hhul2pNK0/NVz/Jk+rqqF+Q79fWtwLGgFJwf7cTvmjKtOuGlGCaIzFxcrpf7i9ja+WMwekGZzWu0ThrTrJgpRzxw5Ff/s0HvvhFx0//2ysur56zvbjiD378E370+iXdZy95d7zHtT3dxTNJRrqGZxsBPxlrudxYxjmwPzZopCp52E3sx0TIBastIYLTiqhAOU2T4LJ3NEbzZ2+2TLNnP0x8uH/Aq8AMOKfom3PfPpjmQB8TaMsUIrvDwBdffvmYTGhLKZoQCjlOGGW5vrrm2fNnrFYdYTyc5pkKWav373eETUfTNvTdFeMYGUdPDjMasAb6ixbTXmLaz5hmRUyGzfYalCEXw74iflPbgllhc6JdrWg2BZrMx4cDbz/OfH275+1h4m78xPqqgnGkSBPqVk6RxrZYAzEMpFQBKGmuCVcCGpa2XQiRnApTPKB0xJiA63qMsRjdolT1YUyRHIVzfjfeEmNgHI9Yq3BWM0wjMc74KJJpaIVtHdpUhaOioYjKSinSRWuM4XK95nL9VF8zBEfjDMYKACkVSRTnkCkqYqZAVo6sMjErwa9ryDEAicY1rFeGzbrB2UysgKF21dPYjstnF7RdxLqAixsRSlhvWG+uca6jabdYs8aYDtdckGmY5oxOHmVkRuicETEE89jlIRWJfMZicGj1lNLy+47vSZGAYlZksyFg0HqNosPYjmKhmEJKYndfTEPWLaEYPnw8CGzaP9A1HRMDg42o5gJnOprY4vc79rcfiGEmJTFhXFCXuWh8cUy0lCwu5NY4bJnRSOvIGEvRGlM0JFM9rmTGpLVGW4sqiaQduXQ1ezx/c+fcubKUUGdcvMf5xqmlWB9q2UArp0khQUwrTpv50gxdnuAR7ZjrxptlgZ7OdH1KXR4LhRPYYqlWOLW4MnWD+J6Zz3kgFLuRgA8ebRTOPyr8TNPIPM94/+iafSKVK5G8KxmiWsR35euLNJXWj79TsqD0zknquRrzppQqsOjsFJzFvk/trcoiTPvkup0FKaoG5RnCNVYZvRRzRYOetZvLUgU+zmcXDcszZujZi3r6enJtx/rl+pzxE3V9vwZVZ8jynsVe8OkFE2NaqRyM1uJdWb3srNVYvcjyRXI44qNoU+bk8fOBm4sVD3bmzs34dkXTrQm5EPQaZQVKb63MwhqrKdmQW6lgkobYWuKYmFNkjJmDhlurOMxJqAIhM0UISZGLptQg7qzBaAHK9Y1h29knp8hHIeinXFjI77P3FVikTiAraecllLKs+jWXly/Zbi9J8yBI1Zzw0y0haGKa8F7OX2M1ZIdGE33VnC0arYQCM04weeF+2jZjncO6hlSU6PpGuQ81GqcdY8nch4SOhtvJMCTDGCLTJ0pTj10TxYLLKWdJbIyeBUmWsj8T8hDQjFKS0IUkdCRtoqDHs4AFBYEeUaST6EYMi3dhxBqqHJvCJ/FOzEWqKoWMNxYptpILSpeTfujyOp15SiovBbwXhL7WLaU8Kr3EnFEp4UNC24SyiTmIRmmqYyOthGNqTcGaIuhRVdCm4ExD1/astxtcIwo4+IBrWpzrsbbDmA6lGlAO4QSKzGShgK6IeXV2NxYBGaYs609pjbEO0yjUJ0pTv+/43u3Qkefsy2tKOLBqexrb0LoVviR8idCswYpMVdO3NJ1mv4fj3Uem45ESv2H8oucdW/7gf/w/oPvha35w0fLFL37BT/8/f00pgabVkFoO8ySbi+6544av8yvadMF123DdNyS/JyWPTx5lLUWL43ucDSkbtKkUCqOJqiNbAxr6LnBh05ONdeHqPBpA1kqMpRA5z//OUKMn7c9cN4TF4LUi8WplprPgw2TYvWzASyWxXEhTe9wFVD61Pk9jP11v7vr1rAq6Vj0SBQXx9/uOU6VUPy8UYoqM84Q52lo1IZqH88zt/UOt/uQPOOdwrqFpmpN6xTAMda4gM0FjDNM04b3HWls5mvnEDdVaMxvRQQxBZkOL0rs+maUur+53OImkRK6t1xMfTdvTyHTRUAwxCZilAhFCCKSYmCZPCAst4jywLR+bek3PEpJPVsETbVOkEvWTnD9tNKbOVhtr0UpQoiHMhGDJJExjvoU2dM7IwxqcEyFrg3BMfc643tK2Gmd6+u6pmLBSA+P7v+ar/S84/Lbjhy9f4/oLSv+C7sUfs7p8gb/ZEKOBCLuhGhznxLaFprf88PqGL97f881d5Ku7I/M0cX9oOc6emDJzKry/D/gQaaxw82KK3Ky2rNII8cgPbnqu8gatVNVXKYw+0s6eFGds29GvNljbcDyKCPZqtaKUREwTlkBjV1xtX/H5Z/+IF6/+gPWq53i8Z7f7wNdf/wxf7jHNihDvpX3YTLTNmr5Zc797h8oalxpU7hhH+Otf/IYYA7lkttstV1fPef78DfvdnhCE5G2sIERnDcdQ+GJfUGrF/XTF3D1nut8xn1MJitRyqibCrWtQQKzzjJS8tMDrKERajlWqjAzFYowjhEzw0nkxNuGccO4okJOmhEghUeKK4DPj0XM83uOc5rMfvpJ7OCem/Sj0AiOSbQr5M41p0GhSqC1lU9CqmgPkIAFKP+6GpcDuIQAtq04My4vykJOImuSAmROZQMgzd8eROUTmGHFG8CB9p3EmQZ5QymC0oWkc280lm/UVL1+/QemBUkbYQSmaaYbZj2gdcQ24puBcom0NTdPRNkLzkYRSZPhUhpQDPsiaPIxiHNA0LdeXN6du4Pc5vncQ9L4wz7lG/JGcJ7Ka8Unjs5TZQWmSd+z3I/u7A3H26DjRlyNXKnE83PPuw6/5f80P/Ms3bzjmgb/55Re8/XDH69efcX97x8f4Duc8RkNI8H4X+fKj50onUpxFlT0OMkDOBdvKYDWVhB8z0RfcIpKt4e0YOfjE+2Pmfs4U/bQSVMuqXoAlShbQgvaTxbFkHxJ1pBqJ8r9u6u+Zx6KxLGaQ1EyqBrZTFZlZKsGST2UjpwBL7W+fXqVAQB8dscopIAqfUJ8+f/LeTpXj02q3FFHiWEALep6JuSpa+FD5cwvxX+Djqs4pmlb0DJd5nK1AlxPStAbJBTVpKxE/hMDheGSz7jFGi3v8OOK9r7Y08g6eUNa/ox0akwS3GNNJIk+bSl4p1Nmf6ENO00yMkXmaTpzA0zwzCrDnfBWcf3x+9r8FKPoEHPNYvJaagS4+HMs1BttYVn3H65cvaRvH9InNi1R+IuZt6ixJq0UYoaYFNaBaZ7HO1c6IwRnNi03HZau46iKXesc87Pn67W/4+PO/IbsNX/zoj+j6LW23wW5uWK1W3FxdUTXUyCiMbemaxMYcGccd+7sjP/z8M4zr2U3Q6cw0ie7nYfLsxol3d4n9OPHLD0eZS+2G0/nKufDV12+5f7hnHu/RaHLKvHjxmmn6JcdhzzgdGI6D6AsbyxQUb293XO/2tNuB9dUNrdZcNB2q7dk+PGDbb9jdf0GY7xmmDxgd0BiwFoPwj0MqHOeZD2+/IeUAZOJ8pLUN6vkrrGspyki1U69xQJGKODR0zqKbnu7iAvOxkdY+j9VgqfzgUgo5Vu3aoqugfj6NTXLKpCwza2sVIsovoKIQ5B4cpxnXKLpVQy6GlLV0MEqos0rNNASO+4HtRUPfO7STJLaURNM4FnnK+mYkkFbQmQ8BqyzGZEKQ1xyzgHFyeQRoaa15/uIznEv4+Z7Wuqo9CzF6Uk4cDyMhQptEfCFlUYhpW0vXWJyFtrbFnWtISbh+1gg6XCyahKs6zxUHoDVNqzHa0bQdq9WGtu1o215apE1H360rCMagtKYoCCnRILgFpUVqT+mM0uHJtfpXHX9H7VCRXIo5oZIMp2NyhOjIRAwWsmY4eIb9iJ8mcphQcaSNmXGa8HcP/O1h4N3tLZcvb/jVb7/h4/2e1XrLNEwobbA1O/ExsxsC9wfP5gYh3RIxca49QVF2UEWknaYAwResqu0GrbgbMw8zvBsSoYCz+Vt73jmyUkGNgFW+isdAuLQ1VcnStkgJZbV0X9WCDiynICi/yNPqEk7b2mPv79HJ7bEa1J/EgMf26CkIngJ1pV58es0+3bxPT1PBLVkEdEOI5CKhOtVAIRVrFRM2j0HQWou19oTCXILgIrcmgc/W9t4yPzR4HxiGAa2ztEKKqPiEEKtizfkFOV2Jb4X2GBMxyHxyEf81Tp3mozL7S8xVIUj0JcdTS7TkR6TsY2v4ad0neq7nicl3H0/k5c6apqdXXkptjxdaZ1mveq6vL2mcYZzO2zUVYGMeW6Jaa5Qu6KIqGngJgnI9jJVHYy2tsWxbw8pE2uxhhjAFHt7f8eW9YiwtcdyxWl/Rr65Yv/whV9c39F2DyYZkNNmomvgU4nBgfPjI3d07/vSzK1bOgWoo0TLqRPJB2pMxM04zu2HmYUrcDoEwhNPaL6Vwe3fH8WgZDh9prQDnulacYeZ5qkhGkTbMWuMj3O9GHg4H1scDz1IB5TDthhWaXFpWO6mIZj8T5ohWMxqZO8taVMwJvI8cDwdy9iiVMKrgvQBcjHVkJZU2SB4qrL6aviiDsg3NaoWpTjdPbqM665XWeZLvF13Rn9L5iUnkGgtVK1mr0xw6F1G7SknAUyhLKZZcjKhrkAlJ9DrDHJlmzzjPPGt72pUDFcmI64W1mpwrhSud4RaWSjQndFGYosg1ec/Umfz5yleKq+vnFEam6QGlDUYLt3RRxvL1tSodayIscn2NM3SNoXGqPjSNMwSlCVWUf5kD57pfCzp7sXkyGC1WU23jaFvRjJa5JzRuAaGpRwGMEqDeX9bkk0BGShMpzd+3EPz+wJjnK8/LTWKkIxtHUYaQW5I2YA0hB6I2lKZj9+WX3H78hrdf/5rdwwPTODJOa4xWPLts+dlXH/nm7Uf+8l/+FmyHdi3X1zcMx4MghkomxZnbjx+Id29ZT+/5x3/4B1gmVPRk74kJfDR43RLQ3Oe2tmYzU9Is9kp9p9Cu0NpI2irudYs5Cy4Lf61KuZzmQnWls7RAT9l+yUBkHvfM00C3vsK2Pa4RRQa1VF21XbcoykjMkgt2olAogVQvVQzkCggp8hrV40b8+LqWDXuZKS5mPr97EPxU01ReYwoecqGxDTFlQpzxczhx/JqmO/1+1/Uyx0OyPh8ju/0Bay1N07Jer09BZqmOlpYoKJqmk549msN+ETAXNZFSCn3Xnd7LQlNXSvaCcz/oUgr73RGyQPmXStChZW4VE+MkKMRhGJkm4VHF+AhiOa1pxYlruZx7+fay2X27Cj1/HSdIeJ0FijuKEoBSTZisNvRdw7rv+aMf/YhXr17y+ZtXWGt4eEhPX49+vMmtlVlN05hKuBeUXtMY+q6la1upBI2tPnSK6XDPVx/e8/brb+hXK4wVB4mk17TNhq0O+N3XfPjwa379s/9Gqr71JdvL56zWW968+oz3u5EP9zv+4j/7D7n/8Jb723ccP/wv+fxHf8zrP/o3cClgTaFZa/7gZkPXXPOrtx/5eLBcrqUNfBcOZx2RwscPb3HOMY1HhuOelMQVZgHBxeQpBeGQFvBec/fg+dWvfsvuEDgcBpxrMNZwPOzY3X3ki1/8LUc/E5PHpIItE07NbK4uSdaAs8y6IRnNarM9qc/I7ZvJJWK7BpWdtB1rh8aamqIuM11rWG0vaNoV1nXAIp1WmIaJUctmjtKSBONq10Tjg6+c28Kq77HK4pRlTpPQFEwU3ILN3N3tabuOfnVFv2owBmKeyEWTi2P2oEzDxY1lddVhm8zDdC9JSyo0ppcqyjixdysJn2Zi1Sm1raHpHE3nKCqScqGkRHGWckYwNtbyp3//JwzDHdN0RxrvQBW6rhNJNqQFSSwUH+msQRlBn16sYNUXnl9pWquxFnKQWXKZNEUHsAHiDHmmpElmhkbTOMt21dC2DetWYcpInidGX6DuJ3u7FlCma0V+ICeO4yCJRsrMPsjeoRRNs+Lrr3/+O+/fT4/vTZbfbjquL1eslQXbSDaAIyP8sUgDSgb6HxnQfkeOgRAy4wy3956mUbTNAvIozONIMQlMEIWLklmtejaNIcTEYU6M+3sOtx+4bBshhMdEcoqYIGRD0B0JQ5sUcSWEValgQJsKJsgFH8RS5IPST+ZnpaT6ON8kl2HbYwtTFcnEM0VsYKInR48mVbLw0gatQXMZ2J1aoOetV1GxoYhKwqkoRCqHpe36KShkmTFw9lrVKXD8/kHwExdnoMktMWW0KYSaPQrpd6FslGpro3G2EaRt5T2lJFw4o6VaXarBpS0aYzy1SxepKGssq9VKsv6Fn9eIAG6u2fNZX/HUSv60mpVKVZSElqpdIYR57z3jMDLPnnmaTyCY0yKuz7zU3n3fCpcKmL20TlPMp0D8uEa+HRA/7dQqhP6ilaJtGtqm5bM3r7nYrLncbnn14gUXF9sT0lOfP4GiVndWpJ9sDX511q0pNM4JRaC2mq2xNM5htcGqAmEm+Qk/TTwcA8pomtaiN4beCGLRh0RMnhRmyJ6oEx+He26V4evf/C3DODOMA9PHXxH2e/J05Le//pcC8V99xvXFmlXriEAoGpPhMGdihpveEWNg0E8l6o6HBxrnCH4i+oEYPYP3+FksrJZbTitV3TkcKWVubz8wziPzNFROmsMPe46HHQ/3t0wpU4h0xqMIaB0JeUZnS6ygipyVBJwk95ouot4CYgSrcmHO/qwf83grJgpFK1zbinWPdmfvq8qAxSxyjSSUkvb/guqONSF0jcM1XfUirQFTC/I3lYJOhbZvKCg+3g28btfYpkGrTAxRZttZ0PJdr0hEchK6RfCZkkBZK7NNY2sfQ1Chpc62taEm3AK8SUX0dKVyimfvK3PY3aNV4ObqiodwoCRZ09YYKDK2aNqGtmtpNx3GKpzLrHsRTtmuK6BLa6IRdLifwNmCs5mmgaI0TXHEJEhZU5V/lAoooqj5nEyFxTfQ2E7AXa6rwDowusP7SjfKswR3FCVZSvrX7iKhuNz2PL/ZknSDti3KWIFtL8tmUQiJmV9xwPh7So6ECLNXfHzwtC1sNpL1NRaUjyLBVsT+Y7NZcXmxZdsa5tkT3t1xfLjj/sNbVo2ltZqSCimJd1RSlqQcWWlu0JQs6iy5zuG0ijRZtP9yNfj9Sqkn6ioL3+2xNQanQU4RJYzlc8n2chWo9ZQ4YysxU7GU6Ofgmfo8uVQtSvX43FXObQmC5+1YTa4v5LzdJq+r1Oc8NfCK/Mb3qf2N1gKb1nIDmpwJKaOStG8V+pEDmbJwfpVA613jsI2qqhyJrpOWVslFFFNqO/TUhqnO77pmHMYa1usNirEGyVJ/B5kvLzzB+pbzYmb6SRAUnUKZsSqzVPyKnMUIdzgeTzfGY67wKbRFnlscIRzWaPb7HdM0McVZtkm1tLSXq/akYf74YW2n6/owClZ9x8XFBX/+kz/j2dUVN9dXrPpezj+PQKfH+aMkEq56zlmrq6yXBECtCp1zgmy0QhC21tG20tWwJaHGGRVniDPv7yKpKNrOcaNaunaFUdVQOAVKnkBpdAzcvv/I4TDw7vYOU6190rhDe3BF89tf/Q3740i+/BOaP/ohfX/FHCH7jI/w8RgoKXPdWabi2RPP0o3CcX9PcE74e3kih8BwOJKr8P0yRgBo206MbEvm9vY95fY9tx/e0bdS/Zb5iJ9nDsORAChdcL3H6EhWkZAmtGlwWUsAXIjoKYgEm1H1nis0rUOlQsWvPGY1NfGKdU9rug5rW4xunqzDVB1wQpS2pzYF65ZUtJByxNmWthEzZKUg+FkQjMrQdk7Q2Tmx2q44HiLv3u+5ef4ZvV6jVSDNijlAiBrrDE1vifmBnCfmMBN9ISfQ2aKdoFuFty2ji9PYRUMhiZZniXUMEokoQj6fc2Zu33/D1WXPi5trpvt3hBwxKKEeKAFwdX3Hat1zeXOBaxTWZdom01jYrExtfRqSsaii8HOhcdA40ZnW1lBUQy7VAzMrtArVYSgIj9gpmqZBa4vWDmvXaNNi3AqjhZK17gvj5JkmjyqDcJiz8LhFIOH7Hd97Jrhed1xsu0rMzqCCbHZVuFlrS/KB8bjnePsVuw9fV/FlRcQSUhb+1TFgavRfuYIhkdBs1o6Lbc/1zZZ/73/+b2ON4zdfvefjh2/4+je/4i/+2V+wWbV0jeHm5RVd39Nttyw0O1O3k4KGOnxeCqRSJA65ouln8ySNLyXWxzLjWTZMCYDl5NMnWqEpRqbxSI4zmoTVRZQapiOpVN8zZx63ygrnl88FHVbIVfk+V8Jn3XDVeeA823RrlfjIacs8+jCpGrCftkO/PQ+UQXoKctNOIZwqvsWVXuZ/YmXTdWJAPM0j6T7SdS3Pnl8DGWMVr16/EKfxo1ApjDGsVquqfiGB8eLygvV6zTAMgMLaBtdI5jnP8yPZPWWUroF9GdB+R0zXWvH3fvJHPHt2hbYKrQ25FI7jyBdfjDzcHYhhIqcKFVpmf3WGWmqyohHUrgoeZxRX/YrLywvSesXheGQK4tgwhkTMAq9/rBXU6SUaRIOycZbNumb7OXGx7rncrPjs5XM26zV934rXX02MclanecpytE1zciVvWiuZv6vBUCvarhdtRCMZstWVPqFEQHyaJcB0pfAnzxuK1hSj0Xog7QL/+X/2n7AfA8PkaVWkbx2XmxUlZUqMdOFIpwtOg9+uyENk9oGvfvUO/ds9X3wY+eIPf8Czm2uK7Wmbjrbt2JWGvutYP7tgmGYGHx7fVakzuCzB+aYF4wrrlWNIhoDi6uaGaZ7Z7w/cXF7QtuJQ/s3btxwOB+ZhT1itYbXCFPHga4zCZVnTFM3gpTJ43iecDmRjcFrRaDHEDSELjy1G8jihw0yz1aALsXKQNQptXL3fZY4nM3BHu17TrlacL8xQN1zQ2NagncX2TjiDJaI7kSrDJoKaKSXjy1zb8IU5eGIRdZZuaxhC5OM3A1+93TGMhefXlg/vd7z7sAPVsL000Br6MqFUIPpE9JkcYY7iQhFzxKoWsWqTWWBRCRUjpQgtpqRMJBFLxJMI6RGglVPi65/9DfHlJTc3F/TO0OoGo1JVQJIZXde19KuWqwsrXFaXyGmCHJiPQfwztQhVWG3oOoNzCddEtJ4xJoLJNI3cV1opulbQ56ve1fa/gHFENGFGG1DKgRqxRqrCpt2y7i65WDc8vxZkubTUPQ/v99/eQH7H8b2DoHYG42xF78kukJVaZDEx2hDmmePxyG5/4DCMZG3YXq/or1piiszzgePhA72rGo1WVRdzRd8YVr1ju+m5vrpkvVrT9Vt+Oj4wHB/41c9+RteKXuDL++dcPrvh9Y8s3bqv7ThZkBp5QWVBRmYLRSgFUDDOfbvJuNANgMeN6REIs7Qhhb8Yqyt2oCSBMOcUCUXailorrG7rbPCxHSoFZjkFtFOgVY/C0rJZL6T9T6LA2Yb+FHVTy9rv4MU8rW6lolmg60uFplB1jiGP/KgZcHoFIQj5exontHmUCRPgi61AGKk8dJQNXm4YI20nparUnCcGAVVIq6OehbCoz0SsbWqL99tRUKG4utxwc7UV2yQlXmbHoyATY5Tnlt/UZ0VgObuep6YywXu8Am+t2DGVunGa5fwpQpLrlCqYYam7jVI0WknWai2NNiitZOOpwIjFfklan+fX7NvX1xjz5GGtZP+uzgTNEgCtxVlXheOBJHMWnQOdLVysHbZ1FCUW1HMuTNFzdz8yeOF2KQcNEdUi6OQknQljwBkFRkj6zmZiKegc0fnAdPjAAyNJOZG3Mg2jbunaDj1dMc6R2w8fztbnWRKiCk6BU0UcGJTMzvpVT6o/76yhcY62b7BWwG1p8SL0AaukinPGYIws7KzESdzHqqGNVDvWOtquZbXZ4o0lhZmGgjVi1UWWmZUfR1SuSZNroTo/JDJGWwFnGFE+Ok/OUoZUpOJSVv7PMpEjk9BWCepWZxLh1HrUZWnfZ0IOhOQpSlG0tFUfdjtKzlyuL6uGhyaWQoyFEAsuSsMzBbEjLKkQyaATpCD7XiXH59r+NTWBXjxPl6+nEp/YpJVSyPPAvFfsCYT5KDuGA9NIB60xYHXGEFHJo6L4xepFRDxneS86SWKbZU9euiVCHxPEubP2tP+Yhb6RvCjAINU81S1ezEkMuTi07jC6IcRC265pmoK1bW2dLvvPv2ayvPykBVfnMCfEmq4XUAOOMUx88c0Hfvv+gbd3A9l1/NGP/5jPfvAjjoeJX/3iF/w3/+8PdFZutq6XtmZRhm7luL5Y8fzZJU2juLpc8+Mf/wn3H77k17+847/8z/4TYvDEGLh58YIf/fGP+Z/9e/+EH/3pj9l2nbQhlEWdFFxqpq3EvFZVkIs2I5+2GSs84kkw1Ep+fpnRySIKQuqPnuBncjXNDGRGn5m96AHqkp7MfQTAuWzt8gdSnTMuQi9FP76GyhB9is2owbSUKul9+v4SCL8jcNZjaVXquhKV0WKcqjVG25OYNxip1mNimiZy5fjN80yIAofebre0bQslkhNY28hszSpW65aYZKYzjaJd6KzMuPzseXjYE8OM1oqLi4uTyPbhsJMMXBW6rpGN5btGnAquLjc8u74glcIcI8fjyMPDjsP+yDhOVdO1uisuAfCTynJZHfvjyDzNIkfVtDgtN2yjDI0xNAbmFNn7mfkMjepQNEqzNa340zUtoQhyNYTIOE4YY7i/v8caw3rV1fbuOUL4KTrUOkkYnJVZn3OGtrPVZFbQtMY42qaj63pxa6BQpiN5emCVRvq15sXqAq0NscAhwIf9zDQFjoeJmGVe1KwaVo3i2kUOofpdVhcAbTWt0qwbzdXKcH9R6PqOn/z4ilICKd4yHUamOTHOkf0YQBt+3m1IumEcHyt8qIAfo9HGYk2gJeMMFCtcuabr0KNQWDTQWsOm71n3fVUzmkk5M80eraF1movOsXIarTIPwTPOmpgUTaMwVsjp/XpDs77kzecBPx7E5Xz2rC9usO2KGBLTYWD37n2NJgWalehYakEZtk3L5QU01tC37snM0MdMKoqmsbKLKpjDhKoYU90o0IWsokzmirRIc50v5xTxaWaOHp8hEjFt5t37r9jfd7y8+jOMcmzWW/aDJ2dFiJo5WFQpxFn4fyUBLsn+kcDnjMZglCPnhFKZRssemHOS4E2RuWCKpDP5PgX0RMLuli/eHoCMbQxX12tJ7GtLlBiII0xqJDWW0jmcrqB6LCRdd99EKhqSyPJpNEaJa4m1hezknpeRSCSkiJ8Gch1rga2m0QZlD+QMMULOVRJRrVitLuj7LZvtBc5ZmsYx+nsm/+E798LvOr5/ENQWjCOVGvxqTvx4+hwpKY5TZDcmsu35t//xP+If/Zv/gD/5yY/59c+/pG8NP/2Lv6wk00zrJCNAGUKc0ETaxkDJpCTgk03fcrVZE3e/JaeIipGvf/1LhsOOyXv+F81/wI9+/IdsLq6QpqiB8+WqBIQjE2SF1NXylkop/Hd/9VeMhx2ifi2Vads6GmtonOXqciszGqOJfiL6iTCP7A8HpnEg2bco14Bt2D08kGNiby1t4wThZ51wwJSFOosLWegJSmm6dcfjtiyVzTgI8lIhnnC5ziybVV+dHKR6liwJ+f1PKsEFtSj8JHsCgKhsUDlymIYKRlFVuk2jlWy4RmtRt8+ZlKT2zzkTfGC320nmVmc5wpOLNMoATrI5MvM08/HDHbv7A+M4ikZoCLSN6IZ2XUvOQmcwWhKXHIUPaZSu85v8xK6plMJvfvVzDg/vyUUzzIHDMPGLX37Jw34QSbL8iOdV9TycJxSP67V+noGQiNljtcbVZEEjmbwF1sbgcpJ2aqEGSMvKye0T/cyQErEUcW135aTysYgjiKLaeaJydr1UFfy2FttYXNvQ2AUgY05VX2Mb+qajb6tVUo5AxKrAq6ueRotPXowQYmY1Rzpd2Daaw5T5cMzspoJzUslrp7jpW3yCwR+42jRc9Ja39x5VFKvGsq5w9b5fVxHuzGYldk/eJw7HAR8ik58YwkDy85N1GGOsyjcaH8XdfBoyk21JjaHreq6upLpZr1ZYY8ghipVRVhjb0LQrum4txsVkhiDi1EYXyInW5apQIhVaCAp0y3p9wz/4h68Y9vfMwwE/R26eXXP16g3DmJirI0islKd0vD2Bx5TVJD8R55FpnkhPR8r4mJlDJKks4CUDTltB5RpLKKO0mlUkJ0WJhewjje3kWmtLQjSQwzSjHTx/3jOaglUNOYFRht5pLt7c0K0t6wvLcHhP9Ef8qCBL8psMFQCYKCGiisYgai1GQ8nmtNDiotCk5PdVWZi5Msq56huUTpAsg5f3kKLH6Ja2MXRNwSgBA+YQCDFT5gztQtTXUoxoS7e6otAQcgdmjVYdhhVOW6xSzCWRUiCVQIoSjLU2tI2T0YnrZCZonGBO0ORsMXqF0g2KHnGSN0zzjmEMpOwZ/ZH94R3f9/j+QVBpIZcqKKem6HICK0JRW4xrWF1cch0LP/kHf48//wd/zp/+2R+gi+Y3P/81XduhGKVM16byokRLUpeE0RBDIPhACp6usaz7lt19QOcIZMLxwH1O/MoaPr7/H/LsxXO2l9csVAOlHiujhbt3Ir6fa2yWwjdff41G5JoEDSm+Yq1r6NqGHJMEtEYTo6iuh+AZp4nDcSC7e1zb0fQ9w+GBFAJzkRlPYy2da7Ha4Iwo26RSmFMCJeCG0m3OQBKF6Gf2+x2ta6Ryiwk/e2JKXNvnGLeg1JZ233l++uSCyYxTceKdUaq8HuakZl+yomQh2xsnIsqucTS5PbUohf4QmeeZJSAupN9lxiEjrgpYKRK45zQxI+crV/skVzdV1zTEECi5VCqFbGxaLc4MhZxOujGn63XY32NUoijL/jhzOM7c3++YfCImMQ3NQKk0E1kP0oZ+TDWW81bPWhLQiFGZqCoqUyuckuq90WJdRBFVm8Y6nHU4bQgpMc9iKJtQ6Do7WSSrzjVNqTNJuVJn1RKLLZM+BUNj7cnnbkk6nBXxZ2edvDck07cqs+osnVE0VjHPhRCrZBWSvV/1hqOHvc/0raVrhNax6hwuSYXVVNWaJWcwWmTRGmslmaucrAwklwguYVRimiCnwBwCqjw1aS05o4pYnpFkzc0p4rEUnbF1lqyVqqqayL0fxRlE0JQWbRtihpRFocRahS1SsRldsJI7kwvMHgoW13ZcbF9w6BvG44pp9GwuL2jXawqeee5ZrTaMwUhb0s8sMnpKi6HtFBLZWJR1T95XzuJjmVMWmgaqoqXlmseQSAg+M0clc4iYaeyCugZdDCprUsloo1ivLdqDznImrJH23sXllqZ3tL3j8HCHnxUxiLaRiNTIXCEttJ3qo+mMqeOhxynMo8emRmNOItTLLbFe9ygV8WEipFkwHylhgcZoGvM4silBxgQJMNmCFSK7tO0brF6RVUPCkYoh54owpcFog6aTyjiLIboEQelc2VqEaO1kVquMBFfVYkyPVg3QishHKoR5wMeJ2Yuk4DQfv2NP/O7j+wfBU7CTs/kY+paPMhdXF/yjf/Pf5NWbzwkh8PqzV6I+3jg+/+w1P/jBKz7/7IZ3734rQs200jaksFIRlz15Gnn79o55zDilWfWWl88v8R8bGqVodeKq6xnmwO2XP+OXf/UvcMbw+vMfAUXQX0sbEh5pX8s05xwUQ+E3v/w5w8NHjG5q8Kz9a+3Q2rHerGkaQ9sZtpc9bWvpVy13uyP3Dw/cHo519rDCH44ygPfhlB7YolC5QC4kBaFkxphYXz5jvb2k6doTyXf2Ex/ef+Bv//ZvWfedzD4KvHv3juMw8G/9O/9TVtstzWpNSQIfNk4G0L9PK2+xGtJq8asvbNZr2WCUQdeAvNlccHV1Vd9zK0TzaeRwOOC9ZxhGrHEyH5wGhmHP8bjneBACcuwzWgkUvTELyMXQthuMMbRtS993YuDbNgQvLhLS+5QboG1FgSeFhNIa9YkeXNc2rPsOZVrGMVOKtIrEyZtFm+cU8JbPlurwaUKvFuAuIUqQRsUqJwVrq+m0ZmMM676jsZbteo1pGjCGt7sHpmHmLhxJOLSWed3l5TUvXjzj2bNnrPr2pG+oTsIGkPNTRQtJCE2lSMijqXQJawxd09M2Lat2RWMrSllFsBqbFMqKjqRRitZJy7G1DV2j6VvDfvTEAgnNn3x2QWsgxUDbdLhUeLE2+AjvduXUfp1Tna01UgWwzDtZKu2Es4bStawV6NY+mq/WE92oQK8Na6250C0ozWQnjiEzp5kcCtuLLW9ev+b+7TcMxyN3dw887A7shwnlLEVNZGXwMYjgfQpM2WK1zJFWraFrGg57CBHG2RCioIdXmxVaSWvzffwASrorb1694MXzF2yev+TdMHAIAZXKyeYqVjEJHwLusIMPb+Gn/+z0xrTVZFXwKaB8wBgR2UdldFbcPewIORJzxiqLVZbe9HKvGUdRGTCUIoHDKklE1jcbDD1tt8JaQQR3m7UkV9Zy/3Fk2O/pVKouLboasUs/MugKd81gnWAoUqVu5SSUDrRCG0tvW3r3uG9oY/jsT35MSRPqK8tq35Ci6NOuimGNY2UMMc7MPjLP4uAxTzPjSqTObNux2jT07QXZbvEJ9nMkFk+LZYrQ0qJNR9tmbJqJaSLnHfmk3mNISeyixDUm4JpLwR+4Bu8TpYykNAr4smSmeSBEz+RHkVPzT5Ox33f8HYLg+dbxaO1zPrYyxtD1Pa9evSTlzGrd45wFpWnbnr7vWW961AdLKpGYFW3NeFoHjUr48ciH9x8IPnB92ZNSQCnR39NO3KFLVJScGIaJt28/sH3xnhAjuqIcgVMgfHyNgsz8lHvXWE1joJQg88QiyhlRZdCZeCgYA3aA9eUPaPoVFxcbpt2RqI4YZTAJzDhjZ48fJx7ev4fKs7voV8zDxHA4UIzGdi391SU5TEyz4fbhA10rPKhpnghlol1ZtIWMqHPE4gl55v3de3o/0IwrTBKu2Gq9oel6yE+D4JPGW5E2HlpQb0WJ/NYSBFf9hq5f8fz5Cy4vL1mt1xjjarsyMIxDtTwKEgTRjNORcRoYxwOH3YEYZE4qtjfw8tUW7xM+JrSxrPoV19c3XFxd0bQirbbMfHb3H/F+IvhR0qmUvxWwHteY8OQSipAiofrYFaQDoJYRYKkBsDwmRE+mskWdfe2TFV5nrVrLrK7vO662G/q2Y7PZcJzFrPl+mjmGiK9kaW1kpmaceK9pJdV3ilKxLaFZ1nN+kqjaCnhxrpHHGWfQGiNQeycuEMZq8ZoziuxaVHTkPJNUIetc155A0XPOZKu46AxXKxhSprGVK5s0X3wcCTHToNlNhYOPXK8Ncwzc7Wa69Q2b9QpnGlk/WVxalDZgCtpmTCmCWDXS2Tm/724crCw4rSlJU5R0R1zK+FLY7/fCm3NWEq7hyMfdjjFEaUHW+bQoBIm4giqZGG11bAkEXxhtBa4VTcqZw3FkNRy5TgFrNLQt19fXtE2Ds0JWVrrQOkNnjXDMlICjDIqkNbmIaEGIkdg+ikfIJCJTtPgp6hMWpRBypMQKnImCLF4MaW0ryjOLbFiq7XK9NKpiprUNje1ZbzancUZGpOqy10zDSJgDm1Wt0J1BO0UxUJRCi2YHumgWP8tcqSjaaHSd81trQak6ipBDa83lix9gSiAny1eHnxN9xuTCdB/R80i+6Ig546NinKPsqKpB6R5lW2KxYNY0/RWri2eYkJnKQPS5VnPqNOfTxoISD8JYuqpmk07tVGMbRFHGYFyLUuKTmYoo5aRKS0s5M4VZ1HWSAtVQeEpp+X3H3y0IqrPmZ/l2G05cylvaRjgamUcyuHMtbduJu7Yy5KyJSTYhazSbTqFVIkxH9oMmxsT+1RW2BBSZEGZwsjGMSmS/hnHm7duPbJ6/xwdPqzTaLtC+5TY8L/7UqXJdjsaKwkGIsTrDy+aaEJaCrxm8NgllLP1qzWZzwbG9ZTYy96Nk8uRxIRHGiYdv3gmQQWn6m+cc7h/4+P4DujGsLy+4uLggpZnZw93De9brNX3fM80TMc/0GyEclZSJJVC0BOS73UeO/kgz9bhkaF2LQhzfc/qEIqFO+7+gtqpR7xIEHmkRhtV6w3Z7ycuXr9hut3RdD0ubRBVmP1W9zYw1DaBE/DpMeD9yfNize3jgt7/9UtworOH6+orD8cjxOGKs4+rqms8++5xnL18LGEJrxmlknkbatmE4PHDY3+HnSfiHcArYj+tPMlilTTUjlUw919kvVIhQUUv3+/G618CmUKcq5XFtqBPISKlFfkA2rqax9Oueq2c3rFcrVusN44ePDMPAwzQzxUxUBqs1RStUtURaqqlFXquw0FqkWkjxadKycP/c6SFr3VU90aZpcVZmTtYJR9fhCK6leCu2NyqzUP21kvsxa0XWmk2ruegLhyTO7FopMJqv744MPvMHN5r9lLkbC9dbR0iBh0PgD1/0XGzWOCuSiEVncswkUyjFYLImZWkTW6OfbKpKwaVT9EaRUBWnqOiswpqAipnD4Yixlq5rOA4D++PA3f5ASLVhnLK01eu6kOdV+BARh8TAbEQYI8Vc2/+Zw2FgczySU0RrRdM4nLs8gcFydTAhB3SKmCSgFVPFMzS5JouiTdmcvS+gIiDlfKMrwE3LTF+Q0BIIY8xoXUCraggrno4xFlJ1cdNA1RKj6Rxd27NarysaPeHLRAwZP2f8PJFCxFVQVutEGShr6TQ5q+tyfrQKS0VMl7UxWEoVwHBCj9L27HpptjdvaFQiT/D1z35LCTOmaOZdIg0zJTsSBZ81oxcecLdqUaZHaQGIYXpMs6VbX0FIOK8Y40ipWBKRYNQVsCci8y63pKSJ0UuCpTTWOXIxqGLQtgFsBfYIkjYRT3hcX53pU5HnLDi+hYj7Hcd/j0rw/HgaUIBHFsDp6aUx6KfIcBzZ7XZMo8fPkdlGSmtxRrFdKbwKDNMDBy9v86t37/ns2RrrLA/3d5jc0dotX90e+fAw8fV9YPirX3DnNf/kl1/w5rPPuLl+dh6W+VcpqRBnVJ5ojabkQEbhVIHsKSWy3WxZrXturi/4watnrNc9dx/f8TAf2ZtUdfyq87pNjMXhL7rqrADaBOLGoswlWSvmvuWOmXL0qFETxgNhsyGs13IuS6bXS7ZhYNWzbRtyzngtXD+/3+FDYdIOP0509wd2h6lCi799ZKQSXLRQRdUl4BpN13U8e3bD5dUNz57fUHLG++mkDarMieJPLorj6MXwVxms62n7LTc3bzjsd2Ab2laqF9c4+PCRVO65uX7O1dU1z5+/pl9vMc6ewD2lCPhB+IkOreZTFV/gKd9xmWnkUp26H9tWaUG4foe6y+nJPrknlkoQaucWhUV8KjtreN6vuVj3vLq84lWtYI/TzO7ujnffvCXOAVBoLcAjYw1aK8Zp4OH+jsN+h9qscav+8TVQtRPy00jc9T1d31dh7JbGWQnAbUfXNmzWF6fz0jqD1YZGW8qxJ/gGH0YJCkW0HEv9Q1KFFBpTaEi4HDkeB663a3702TO+2mXeP8x8fSzgDDdOYapU1tWznj//yR9ztV2hcpBNO0vAruUQMUWMKVUAXILb+ZFQQsouitSs5DJEmb25nNluOqwuHHc7Pt7ecxwGTClSOWrFMaWKQyiokE5EdrFKUuiQmbwkG7kUURcxhoe7A+vVnug9RkvLUSFO7jEn3r39mvu7j/zq53/L3d0d4zSSK9BryZpzEeBWmhLTcZBksl7IlCMxFQKBFCbpFumCVrIf+JDxc8JPGbcylGxJWRF8Rqu6XhdVxiAasS0NnetoXUtRYBtBSosS0Mx4HHl2tUGXlste07UiT6a0zOV0KULNqLeAL0WUb1SRjoJr6Tp3WoQpxk/uF4VNLfN05O3XOx4eEnGytJstTetonK3VXMHozOVKy/6gwQfN5AtTSKR8ZJxvKfqSkDK7/cwwiUvQNATGZkSR8POBTKAQhN9bIilHCImcjaCptQPl2I0PpCqAsGiHxpxwTYt1DVcv3ojV1BSZ5wim/+594DuO/15BcAl932/sKO2RECMxBUoJNA5KlpaO0TKANwZUzuTgGYeBVDT39zs2DbQqMM+Bw6AxZuRuP7MfIz4r7u4PdO8+8ptf/5bNesvN9XUFSurao1/a+Ett8fQQZQ5RHYkRSswSMErGlESrE70prBqNCjNxyEz7HX4SDUAfonj5LaWHNrR9j4mSadm2rb1syaBkhqcxIKK2MZPGiSkuzsiSJS03iMoFU2SMPc/iuTjHSPQJhWGeEs2Q2B/nb6urnF0vmZeePiLVNqWu1IycE7P3UiFUcFEuIpquqlwaShFTwHuRc9JV6qtrWmJO9KsVrjHSdlGKfrWmoLi8vmG92WKbRpBpMZGL9O1DbaOG6AURnMuJIQJQPr1gRb62ZPKximJTHiu837k2z74Pjy3zx/8LBkWjFJ3SNKVgU0Z5j/JeDGqVZtu2XK/XTKkw58KcOQlfCzk7MM0z4zTRNg2rbkEASxX4aatXUdu81qKNkfNapdGca2hcdxIlV0oJes4YGu2Ym45oGuJcCAVMAZSsL6UKaIM2RQA9RtNbjaVAEbUgayRAvH8YKwlfURIU03F1ecF23dP3LX5My0lD1/nSo6KOqgAeEfc+P+aiMMqgjKOzrcQXY9gqS5MyLZBnzyGOjLO4nq9dQ2esdFJiYAEwqhCkRY+8rwJE5H0kJUmR1dLZydPIdDxw2O1ZrWXGfS6GPwwDu4cH3r/9hru7W8ZpoNTqZFm/4rcZiXMkzIu0TL3Nq8uHKmIfRyqEFGmMkbFK1ujisNphzQqtW3JxdXNXJxumajImNkEVnCeocHCdAMhCGfE2ofXIqndoFK4R13nhS4rC1KL1UarohlZSFSYjEnBZIfPBLM4XCzjudHuUzMPdR7IXubqmdTijadcrjNUUrVkIFUVp0JZCFa330pb0qZAeBoYxM86i6bs7DhQ0q9WKZ1dXlDQzzy0wCxJVJUKQSl/pInKXSonyixiRMs6R2UeO4yzOFwqsc7Q54XKGOeN95nj0TKMXvuX3jFB/5yB4PlN77DKefW35sJxNDYvCV8FbrSPbtaWzUjI6q7FWuDmkRAqRh3uPPkbavsfh6UxinDI+TOyGyDd3A0OAiGV3fyDrt/zzv/gpz58940c/+uwxCH6ihbwoiJwvZucMbVODQRYItsqgU0SVRE+gV4GuROb7W7yC4+4BP85EH4lFEFrKgENg0tcXV2Jzkgurtq88RaFGFCUbRqc1VikMmnicOYYDFLDW0fU9KckL1olaaRbCNDDFwNHPDJMX9R5zwDV7jmN4Wl2cHyf2e52JFVFsUdX+aQ4Tx/GIvrOsVhu6tsM1XfXhEzV2pZF2T5oZ5oGHhx25boJdK5t0t+6qKDWUnNlcXnJ5/YyrqxuRVjO2JkOJ2c/4ecLPkzgCDAfmaSDHfLoxP71Wj5dSZmqhGgA/8jt/RxX4Hcd5AFRnPAqnoTOGldG4GNHjSCyJ2DjYbLi6vOKHz25YdR3pq6/ZjRP304yxsoEKp23GKMVhv6drGtJmxQmyU0vyT1+pcQ7jmgqKcaf2aNO0tG1/arE659j0K5yxWGOYujXerfCpVvkaMLK2lK4IQhRGW1YNXPUytyQlPu6OFK3J2vDL9yPWapxWmBT57FXPn/7xM7brjrZxJC/BWRdBIcYFvYkkTMZYLBljH7eUUmCfDBrHtuu5Mj1Oa5IqrFtZB4SZh3Hkdn/gME1YpXm12vCsbemMZgyzVBtGk72XCgBRa4k58zAbhpiYYsL7QGcNV72jDHuG2w+8e/uWV69f45pG2tNFwFDDceDh/oFvvv6Gu4cPTPMIuoKSnKiWlJwFqT57cjwfN6iq2KNFdzcXksoiGt84rLLoYnHG0uqGrt1iraUUJ4moKigi9ZaSToKyWN3J1SpQErRtz2a7xedIDNC5EWMa+RlTTrqylEopMppIZBHdsEbaj8UZslb4kqAib4MXD8Bw9r5ySnz5q7/B6oKfH7i6WaOVoe96sSJLwpVd5u9kESoIITPNgZjEW3W83zHNHximXzDPgeM4sr245Or6iq6x7J5tubzsWW8brFNYC/N8BJXpugZjDFnJGlNagS4cDkce9ke+fvdeXDGM5tnzF3T9RNP27Pcz4xjY7SbG0fPlb7/8vlXa/6/t0N91LJtXlQlTmrZzPLvZ8qd/+hm7a8d4HLm7E1HtROboA+OcGcfC7j5RbKbdHHl1vaXthWuyGwUCu/eRfrPhj/7gc3bHQC6G//Q//i9Zr3oap/iTP/0j2rarEgWf8AbPDoXoFRprOA47dvcDw9HzcOdl1qYKDx8/1PbeomIh8zcVjJBAq1KIMsgoNifyNIvbcoHJWknUSiGULAN0q+WhgZrxpCT+dourhWjqibwXNXAd4ywuGSWBdbi259nzFRZBiP7OGFDf/mI3VOpcQOuFV2kpRYvH1xRJcaZtnGRS2tC0rciTTSPaNKzWlu32+tSaFIskTds9ggfEeLcXfcuKflVa4wfJJfu+Z7vZQE4Mu/fEeSCEmRwr5yktKN+nbyojGf88C01lnsSVQildxQn0WbLze84H5wFQvqgV9H3L1arlpm/ZkDEposPMcH8PfmazWfP5m5f8YLXGPb/mi7fvGH/9G3I1RX40Ta5mu94zHAcexRsyaBiH8Ummal2DazraugEbI3PArl+JEa1R4gTuHF0rfDqtYHP1EqMUH3bvxM+vFGKxdFrRNMIl1AS0dXRt4VIVNtuOIRS+2R352ZcHvnkI5AQ+ZYKCi77h4vKCP/z8Da1t0UXRuE4q3bPbu5RCmgVVq1PBIrOnp6faQjGQCl4lak7HNIt5s0+e/TRynGd8CJimoVv1XPQrLqzDaqlijdIn4fVIJoTEnBIf/MQ3hz3+OFCypzWKF2vH4A/sPwb+6i+FA2isYbXeVnWTwsXFlsP+QpIP69BRgHEaXQurBe2i6kb8dAkZLXPgBofXnlI0pjSoZFHKse3XGNXh9Brb9jIDA0xJ6CKGTQVDLgaLxWLQqq0+mTONkRbo1DRMY2AeA5OfWVvRKdVNVWIBiKmODkTFa9nnFooU1KQlZdIUyNWOrDQCclmOUjKH+3ds+kYoNKseYyzOdcz7xBhm3r77gHOu6rw2VcO246LbkEvhfrcnhonhOHG/21VAU5E+ekqEOTCNHms0MUXa1tD1wjFXWuPnqnGsDLEocknEHBkOA8PhwLjfkVUWYXKdsCqgCxz3HzgeJw77SSg1avzd9/8nx/f3E/zk/2WJP60NH39i+Xf53zWO9brn5uaCNNyRg66Zimxqk0+Mc2YaE9McKVEzDBOzjwSnicUxhZnjlIgUmq7js8/fsDkEDseZL7/8mt/8+kt+8+vP+OEPX0s7IxdyPncrz0zj+KTFtsihhRCYvXh23T8coFR+k/SX6oYpb9mgcclis2zuaMm6G0SeTcWKbFPgtEFlmcklsgAVrCY5Ra6byikIZgH1o9RJMkjVyJVLYcxROuiqYLoVq43i6pncTOU7/AS/fRGX2lxJ5m4c1nU412JtQymq3oSqokflRhJXaeHjGONwTtO2nUCua1aptRan8wVCrvUJ6bh8rZRCqq7w0u4zKKMrRURXqbGnotnqk8Au3mMCYNH19f3u1nw560w8eZbTeXj8jJNaft/3XF5uuLQa/IzfP4gY/Dzjh4HV5SV903C93XJ/ONA4xxwebbMWq6UQAtM0clzMVBWnFv0wHM9ekzrNsqx1J8fvRZZO2s5SbTlbeYJaA4mu30iga9Yi5JCEkJ2MImohY6MzaI2zmh7NurUUVWhtqk4VisZqgc8Dq3XPdrPm8mIjgBjExULVdq+1FpsSNllU0OhcMCbXLscCPqvXPEtSE0Jk1qZCY+A4TwzjyJQ8g5+ZghfHh2xPziMF2dSrzEOVWJR7SWhMYv8jiMt63muLz/uJuQQGr3i4v+f65hn9SmhBKMQFoW1rC7qeZxZeZkVWZvEoXPSD45ldmVYi6mC1QRdNLgWNQRWLVhZjOqzucabHur6i1jM6S4cJqmZpEUSqRqOVlTVEIsWADzN2bpimmakiqVe2yFuoVOhSz0WlCnLapCq+4LwFnFISwYGaTHyLMlQQr0hnKjldPEaz0mSliUUxzAGXIGPxAZwrKNPQWRHPWHw+S51paKUEMb14xPrAPHmMEfStQiyUnG0lQVFgtBORbGVqy7jQWEfXNKy6llQi1hlaZ2hqS7h1kBoILVhr6Nt/7S4SnBbl8v8jQ/C7f1LVfxdR5MvLC549u+bNy+f88q//mg8f7hnGFoigMgcKxyFxv49MQwKTuf/4kQ8fN8SwZiwNvjhi0fS95gefPecf/7v/FscJvvrqI//1P/u/8S/++d/QmIa/92d/wmo1kPPM4WGSzbtx5JJ5+/7jCUlZgIfdEaszPgQRS87w/nbHMMxMk8c5WQwpyxylJlaYBYFYT4LSYEtBdFMQUr4CubVqIqmV6CZaaS2h1cnFQjI1yehyeVzIKnHyJpxVJimIStGttlw/g5efO2y2J0mv7zpUebxWWsvrcLZne3HNs+dvePb8FdY1TNOIj4kSA+g65zKab776BoDNxSWX2y1dK/5/IUZU8fRtT9t2vHzxohqFJvb7PdGL6MGySLTWHPZ7Uoy0TYtvZMMng658xVr6ymt+UqnV5wAaa7ncdFxfXpKL5jjdVXcRoSc/rsqnhPQTKaLIM+lTSQiKUudomu3lFZ//wQ/5wzcvSNPA7Vdf8vDVV4Rh4P0vfsm027O6uqbZbtmiue563vmBUOcsQv/xPDy8ZzzecvteCa1Acaqm7vfHE9oRJJgveoqaiNIK4xxFaVKBRgmsvW1bmrYRdZSY2F7csF5f8PHdl+x2dxwOD1xd/whU5v74ji0Fp8S9u20tTaNZNY7NquHNixvW20u+vJ35z//lLYdBZOf+/p/+iB//8A3XVxfV7SSTs61rNZO7Tlr0SjGG+ZRkmizI4MddQFwchmlinj02R7QxFArf3L3j7uGeWCHuKSWsMrRKE2fP+2nmfSncBY9RWt6/kvnbnBLKGZlRKbibBsZ5JJfMwzgzzjOpiD5pdoGvv/otxjq2V9c0rkFrRb/qWW9WdH1PP/UCwljUYpRUf1optOpIocF7zzw+cs9EAsxgiwXxdsW2Da3tcaajxA6DQxdNaxu538lQHBQBfuRisdmRi69r01TATAI/ctg/MM0TH959ZBgeOO5vuVhvaKwBLdUdaUkKBGBhqn1RjmLKm0sSycsscFVVUevGaUEZn1nqKBRarYnRMHnPFI4oY+g3hpgdmDVF9cxREYfCPO9RStE0e64uLzBGc3d/hwJWfUvft6c7zzhRlNnd74ghMhxnNtsWqx3d8y03zy4wRjH7gdVqRdM4ceeoTvKv3rwhxMD+sGe3uyPmwPZiUxV6DM+vtngfq2yfos36CV/19x1/hyCYIMe64BfFGPU7UAjn4bI6K6tSzRPXlCILoFtd4sORGCamkFF2xeV1R3OpsE3D1bNrPv/RG7qu4Re/+BXKiIPx85sVn715wR/+0Y/Q7ZZXn93xk//0j5nnmZ/+5X/H3/x3v+DNmxueP1vh5yOlKFbbHu0s23k+za0AXNfTr7e0pWW11mzHyP1D4eu3H9lNt/goIV9abPXNFljaW8s2poFGS3PRKVXd5lXVo5QMp9SFqqodjkJRdCHpRFKJYhcag9wQ1D9TTjZDAp7RKGy7wroelK3cmUVv7/H4zgSlLDeCIqfIcNzTrzd0FNq+xaYk6E/E3aGkBJVXtF6tyDkxDkepikIghIg2mpwCw/Fw2tDmcSJXx3YfPM45+r5n1XWVaya+jD6K1qaffUV9Svaq1DK3O0+1RJS8ZKEc5Copd5o+q6dLcZkfqlNrXr5aTt95/ApK6Bg5ygxoHieUMXSbDS9+8BmdM8z7PYd3t8zVsHf6eM9xHDHHCTUHVBa7LV2KgLxSpJApRrJyoFa6UJ7MmJYMWB4aqTI4qefo05xQaV2VVDhVSEoZnr38Af3miovpyOtXP0CTmfZXxPvfMg53JL2TSldB3zi0deAcn796SbdNPJSGu92Ej4Wf/PEf8vz6mqZpyEFQoTHO0hGoVYTRRlzAXYtWGl9AFGw+WYO1JSdt7CgVdZ0taWNolSXlRFTS/q8wXeacCTHy7mGHM4bWiEpOzIkxeLBa3OBLZppm/CygrqKEmiDtwYJKgf1+z4ePH7m9vaXrOhpnIRUoipubZ5QSGQYrlIly9rtKVbBQreDP1pWS0R4kaKqx8arrxfOxXi+KdIFKnik8IinlbBThvynQcdlT9AnUkksSFwUllY3W+tQajEUAXLn6oBpVRzJakJPiayr3Ugas1qLrZgrJCGDGugbjHNY9ytylXPjy3ZHVqgWlmHxAqcSUB4w1krQtaHFVCEUAdsdZaA1d59huVjTW4Kwh1k7R5BO5GHKCw27CzwnXjMSwonUN8+jp245+1VDocM5WoGIUVK/WaGtJ2eCqfmxMUboUy/pKQFGkIl2PVj9V+Pl9x/cPglksRqT+NnLBzuq9xxYTwCO/a8kSVYXprvoeaxqs7dhcPWd/UAxDYZw9/XrL9vI5pnO0fcf18yvefP4apTVN35KChWS4utzw/NkVr9+8YnvzgqubG37y53/ML3/2G7784it+8bNfoYlcX35OSqHyFw1N1wlZ/7SpKppuRbfeolRC65aVzzx7P3J/nCi3D7VnrmrLQT22EZQMiJOS9oSuVZ6tP6eMro+qlI5Uh0prtLNo5Sq5u6BiRtnqKn/a9GWWqRbod6kbnxKwQ9tvcO0KpS3lpOH6e446C1yku7QSEd9xPDKNoyhIrHq00SfPwKVKlbmAo+0afA1Y4/Eo1XOMbLYbSk6M4/HkRejnWSpfTW1ByxywqQCFeZoIPuD9zDRNwvcrNcjVhOPTVighSZErAAEAAElEQVRQW17CE8rpDJwBj73TwtkVfjwB3zLLPa3gctrkckpEL0Ewp4ztG/qbaxyFqe+Jw8xxf2DcHRnynjkk9OzRIUpQcqqSn4vsxhppa5dy2ji/hfdRJ8EPjBYOrq7tKJS0AXVFcYKq50id1rHWhsvrl6w2l4Qw8/z5K0opHPsNd34keE/SFqOKWAw5K0R6Y7i5usCtFX8wJ7brmSnADz/7jFXX4ax0GEp+TDhSRYWKCa4IfINU4Qsp+8lRE5ml7Z+BOYqRrbMC7kkp4VF4IQ5L4lQyU4rcD0ca4+hdQmtFSInDPFJMBXmlVB+ZxjnqXljbhBmVEsNw5OH+nrvbj/R9T9s0YvoaApvVmnHoyTnifTglaDGnWp1rTGNrC/PJJZN7KhWcEv5m1za1o6GkWku6OkEIiE1VdC8KynKdS0WTFqEdwTLfE11NceiReXepLdlYCqpoMovlkz61EtOJe/E4itJKS99Va1RFHzdNg67KRMuRS+Gbj0c2c6HtWuZZOJZRT/RdgzaqavrKPpJVIeTMPAXaZkKpwuXNllXraBvL7APzHMnZ46N008ahdgWsxhqYthPRR9qmYd33GNfVe7EQo6x/VV93yVray9acxkfLDD5WgQ2nhEfs9PcPbd/7J1WcwB8Ftm9E9PrUnNZLT/DTXUvVC6yBiLWafrvlz/7sD/nJnzv+nX/6v+Knf/XX/PwXv+Qv/tm/4B/+o3/IP/33/ymvf/CaftXRrjrW2zXjOKHI/Iv/5p/ztz/9K66eveTmxSue3Vyzvbnk6vqS//3/4X/D//M/+o/5L/+Lf8Z/9B/+P/g3/tHf4+r63+flZy/o+g7dapqVoZvtaWdUCq6fv+bZqxd1HiG6ha9/ODPnSNaZcZJMyVYzWlWBLAtXbZmDGCO2N7rOWFT9eLELUfXOUVrLHKPaFwk4pYp3U1Vc6uenG62CEJTRp1a0c4LgtG1bWy3f3ZiuT/H4UUFAMDGg1EgYHcfdAykkUsw1Uxa3glwk89+sN2ilCNNMmEXqLKUkG6GzWKPJKXJ/f6RpujpHDHSuk2oiRhor6vHBe/w8c3/7UVRmgud43AsKlZP9orzaU7R4fAeLnmkomRADMcVTNijjtm8rwDyeg6qbuAS8qoWp60nWCjqtyYcHHn4b+XXxXF1d8OLNC1bbDZvLLdubKx5u79nfPfD1F1+RDgP9lGhylYZQmsY4WiNVkmTF+lRZWGvJpeA8T66ZUCQEeGXN0imQJKnkiJ9nqcpzRjdOAmMpaCdao9cvPquapg3WSiVw8+INq7bn4ePX/PqnA1ZPaBvF1JSCDzNjPHL0iv3es9le8WJzyfX1c4w25JjQRqT/+n5FHkdi9kQfUEYUbvq2x2hTr1mtgM6WYiyyyRglMy+lwBrYbAVe36rFt3Li9rAXNRGlyDEQ/Uwpoggkwv0Ni5VYGGdpvZd8cirJSlSrnK0Vc4EYC+PhSAyJ/2p/L2ID1rC2Rgjxcebh/p79MDFPURKrlKUKMQrTWP789QrbFH5dg2sBSvKUWFAxcdH12KbeByWTiSiT0FbToKRSrR0PlJDWV/0GrQy2JsLLfpmXKtCHkwNHqmIL2moSgkhXqa4fI0T5nEXftyQxNXZVk1mc1kW0xFqLM5J8C/hOEtbHeyvzL3/+GzabFdvtmtWqo2lF89g5J44p/VrcJ1IS2zOVsdaRS2L2E0Wta2dLUUhok2laqVJDzOzGiYKMWq5vVmgjQgaNk4dxIn1ZiqzzUsoJKLecpsZZsrFVoF4CoSHjjKjtaK3Z9E/9H3/f8f2BMSlACrKhl2VbqZlpfmyNluX/siCVcq2DpDSP/1/2/qPJkmxdz8Sepdx9yxCZkZmlTx1x9YUgQTRImnXTyDlGHBGc0IwTGvlL+Ac444QDTjhhY0AjaWhrskE0GtbobgBXHVHnnNKpQm3lagkOvuW+fUdm1c26wDUY2q6XRUXklu5rLV+fer/3jdIYPJst+PjTD7i+v+Vuu8OWBauzJe+9/5hnH15RViXGaIqqoKwsf+fv/jGH7ZZXL1/SB0XvM5JSCSz7gw+f8Ed/6/cgJf7FP/9nNHXNl198ydNnV5RFQQS6uuaw3ZwAL4wVdFhCEZKk/qpZxeWjcyICu0aJkdM5jeq9J/iU63BSPxCghx4BNKIeLkCLcQzJtR9jQJu8GDNzy1g5VJxA0XKxf3ivtEuIAKjOG6fW+g106KlA8FGf75TwTuWoSWVQiCbFQN+nURw3pgS5jhJSou+6kVTbOUdRFhhjM61aR1VJw7dEkLKhlYWQgXdtS33Y0+X2iLat6dqGvmtFbX6MhI8Iy4c5/QEAEXN0kkarqU5en9LkFshpspzYzuMjuQydEpZIqRWl1qycpSSh+476+hbdttgQCJdnFFWBLgzFYsZcJVb1Hm81u+DRoZHUtZLviCT6IJFNzIAgOZeOhGJft5M5QQxfBoSgJkCsNInEMoNIiCan1TLhtrMUrsxSTE4Em2Mk+kA1XxLjFedPf4Q6vMC3N/gsXxZ0wfXec9+CLiqWqzPOLx7hdCYA1SqrJwhTj7VO2iNCyowfmfQ7DmCSU0JmkBRbH4VZpXJOAFOTcoTNznSICWsOAn4Z6uRB2iiUUvQxEH1PkNAqZzVylGR0dgTHxZ/rvOJIhtCTmkTb7LIjqnhv7VgvHE+u5uxqw26v6bpW+gydpihcVnxJ0pN5EuEmUpQ+XRE9FvQqyFwnYo6K07geQvQ0bT0STFflQlqrlMIYSQXL3qAhJnzoCNGjfE/XSpTqY8y6E0NWJ++/Od0sJPbp2P6jNW8YgUHUb7hJ3iiaCaCw63qMNdIDqQQN27Y9h0M9znvhSqJJxODxfZ1Tnx3G5tpwlm2zThh9jE/4UAgAa0SxviUtkgMqla9V6ZwJG3LV4zQf7xOlc5nI5hKBfpCR+J7jnY1gDB0pdChtj5RpKRzFa+Wscr44pw4BpSxDai/GRNslbFGyPF/x/ifP+Obla15d3+PKksVqxuMnay6uRBsq9gNAw/E/+Z/+R2zvN3z5xdccdnfs61xMjh7rNE+eXvD3/8Hf5ZOPP+Kbr39Dip5f/vyX/A//B3+bmSvoQs9ue8/961en6EOtRa5DS+NtQlHNSp48veL8Yi2s7Fo2G6UUKabc5C2pIbns4/Mq05MNRnBQEQCOqTutSVoQWKPHr3QmglY5Wksn7zsK5EruLOYOYpWVCvRkYzleXJ6MSbp6YH5FiyEmn7vL6Ku+72lbQaMN3xdn4l2pzDIRQqDveqqqYrlYjv/u2i5TYFUSWWkpTs+KAu899W7PZntP17ciP7TfUtd72rYWMdw0GOxxRfHwJh7SlzEeDeHxdcO6nN5ax/U5fq6Sb9EkbEoUKbK0lrmxnJclC6UpYqS+vsbf3dNd39G994TZ+ZLVe48xs5L5rOAseUJp2fgO3fbQR5ISZQMfA43v6WNAg6SOg2g1ojT7Q3tyac5a0XZMQ3pLlD0ElBIgWfk7CYemRgyetY7ClRRFISTOOuE78H2kbRrKakFRloTwh9x/Bdvnd3RBgSnwbsW3u3vumkQxP+Pi8jFPHl9hVE41KtFljIiz6IqU1x7jPGlj0CFk5QxOaNMA+hBQXpqsS6Uz+pXjRqykMdrGSGEcFkEYdj7QhUAXvDC5GIOOIYPEBqStGlU3dK6VDpiy4RbXmrxJ9xwOG5ESipErV7E4W/EHn17w6rbi+i7QdR3FvGS5LHCFpekC1/e1NLw/3FMztZoxFpuzP0QxRBK55rSkikQFPkXqpkYZg7VFrruLA22NJmoBxFlrs3ZgQ/CelBSHfUObkb9Jmdy5cTQgcaREzHXnIeORwT3DfiLDLc72kF2askwpoChcVvUJtE2L74305GYE+OGwZzabsVwuhQIzJbzv2HU1ffDs62ZkjJIgBQqt0MqQ8vU1nceHyEAon7U2iCR0HIz0wAEtVyRo/wgETkXQOV6XZiwMPaxNf9/x7onTRI5IYgZoPDiJidchdTNEbHfIpeuSroP7jaePBVGV4AxdH2gbz9lqxWqxYDZz6NCRCDk1lgDD6uycy6snPH3/PX71iy19HKYtSsokKNarGbNPP+B/87/9X9Mc9vi2ZrE8I0ZNUVSURUNRFCeJQ61FKzAlQ1FkPT1riDEwZVMYrysLfPre4/2Ry1CPnsfQKyZ/H6V0JuwMKgsRKy1GLEeAMebFGU8N4PAzUjopNb5nMLQPjaBCsn4nqVByhJ7AmIKynLM8O6MoCyKe6+uXdJ0IF8sNKOdR55SrUoquG1KhGh962rYRSHrfsd/vub+9pt4X1HUzjmGKEgl1XZft1DEyGmooKC21rokJfFufoHUlzlX4TKmUtCUqL06XmlzrW7Igw5joBFYpKqDUirVzPFrOWJQFF4sZs8IxsxbTekLb0W63vOxazPOC6uaaYrXAziv6FOi0Js1Kgk4kFaR9pHIUhaNpa2EC0YrWB7yPNF1PiOkkEgTJl0TJs42bu7FGaspashVCTm3EfGuNKQu0K0TmR2Ux4QBRa0zpWJbrrPzgOTu/ZP+8oGt6djrQq8hOG66efcKzasXZ+pKyLLHW4Lt+rFuWZQk4SAJUSUnhbJWjnYi1BSGC0i1KhTey8sLsI/Vu77NKQL7yYY5D1stUuRzgjSMYS7SWajZjMZ+zXiw57PZ0uVVAwEEKU5jRELbZcRtbHBLZgZAooiwLTAwEH+h8wWan+fzrA5t9TR/kvReXa3708XvMZjNeX9/z6uYzfvtyNyzbcR2ZymEqJ2ouRoFVKGNEcNsnZoWwnaiksEZRFgIsUxn+r5OTloqhnBTz0Blhugox0rcNIdbc7fYcmgP7+p5HT89xZYFzKhNwh0xuLkAppcUDGGqaSgkh+HDzj7XFkDMMp6EVVTmIHmRQlrJYU0otr2vZbrbMZh1NHVjN59L6oBLNQdKxd7ohBEPvFbOZUK3NSiGEl++XNG/npU0khF76g0PIUazwGQ97wFjGSP24F0yDjWHvGzNHHFtl3vX4AXqCx1Sc+Nvq6GMPEexksMcAZDw0PiR2dYe2FUW5AG3FcwqJ+WzObFbhXCGfM20TUDr384iSPdpkJvKhWUYQUSYX2j/50Yc09YF6v8MVhZyrMdiioChLphZ8+GuARxszpHNF4244xLDlFBwpG88wvndg5gRpkmbirQzGRHL0+TGtSQ/SoFpLWmTkMOT4/hNDqwaEoxqm5c3pSg/2I8UJunWobZDA+240UlLv86QQpL4REykIAlRpI2nLGDFa6jspe7zeB3zfsd/vaBtD27ZZhzB771EozqzN7xvFguOwfIbVNBnTh4ZQjS0DbaZuk2h8cnOkY6R33LTU+FtrAS85BQujqKzmrHKs5zMWpaOqLGXphJqssPQG2sNB6h2+o7tRFN5ju47oLHXb0Xqf14QSVYLSUjpL33eQhEx5WDeQuUPfkoZKcHLOkqGwmXF/UGe3Iruk7UiCLu87zrboGCItBUEKEUMvqDaW4Du8gWhKlqtzqsUZy8UZ5MgzTywJxBAnQ4x+7KFLSO12Wn19E8krx5DCVaiRAGFQWh83uBiF/i5JTNAGiQJ9iNIobwZxYT32Kk4jQSlV6PE8xnslgVchj3vEOiG4AGg6uN97vn65FyPoW7n+mGnAvER1RivakD3HyVwpI+nimCBk2kSNlA9krfejw8VQIlG5P1e7rD14uupHfZ4ke2IfPb2PHJoDh3rP/lDT9yvp81ODenveB/I+PDrcREw2fw9XmvQMvhnIHOdLQFwhSbbP95Gu83Stp+8CSvUoalQAa0SDMngpde333XHNao3CUFoytaHs1ZJOFYDXsO8MQBfJHE5PdnjbAJp7kBUb17xk6dA6n/9fQySorAXnICiSsjldA2P9Ql6VB/l4omg1Gom6Dby62fHJh0+4ePIBSs2JmXrn6vFjzs8uqWZLUCK/ZLQFPVyQoLyub26xrqTIxWjUIFOa02lKc3a5YB1nxHDObt8TkqRtqtWa1UXzwMBMNp78uBT6w2hdjlGchPAygRn0M7nuvPXmqCadiL0PKc3Ru1Ga6YYx3RNPJ/cIqhjG+7jtD2OTxlTI+BnTT5+kh4Y0SOg9XdOw1zsOhxqJRDPiKknNYzjnrjU5JWxH9vcQvLS27LfjOcYYuXm5E+MZovCCZg04pZTIAI1pY0XwfWbyTyP16nCNw7U/XMxdgO2h5ZsX19xtttR1B+nInmG04sSaAEapjLDTWRVe+CXPZwWLyvF4PWdZGpxVoALlzFFUJaV2mENBqzzt7Zau7dnf3KL2eygKYlWxa1tu9juIkcoZLpbSaF44x+EgTeJdJxRZSiv0rEJ3wjnLZItSYwXryIHqbIlzJcZJ3VX0BGdj/dDagpi0bNjFcN0wq0TzkRBISvoTjXPMFgvOzs7ZXNfoYsbq8Yc8vnoPV81FmLTvCUFaF6JK4CXSUyR8n3CuIKJoO6nfxuCPjszoJB+PlKR0MLRU9EEKDnFa001x7C3tQ0+f4HobqQ87et+BEcRu23eCutbijFkt9/R8Ph/XyLDnWCM6eilHnyH3y82KGSYEtIbrXcerTcNnzzf4FMd77PZmQ1t3WcEgsZzPcE4Epqc3VzIKbxJdkGhKeJcCIaerm3pPtCWqSLkfWM6jLBylq6R2S05vh0H7A+l39ZG29bTB0/Y9t5sb9vsD+/2epl7TLTQ2iBbh4HxLn2OizuMtUdyx6V3l18hYJ/oQcc4JyHEyX9vdXuSmnMuGUnE4tGMKUlHQ1oF6v2V3d6AsHOvVEowhJsWruzuaVkjNY9T0VZI2EgUQJaWb68kheJq2YbORfsiFnwHFuCdLJC99pH30xJT7HtNxh5N9MYsLKwkgMEg0+47HuxtBIzL3gpA89meQF7UAYyRNp3JuNqms7K6kv6fzPXXbsDpbcnZ5BgiXZNc2mNKhR6OmRD5DWca2DCK+C3SNZ1bNqaoZ2oqRFM9UxFpUvhFTZu8QERdQuXnyoX8wTVWOf49F2DRGOymF8fnpzTDkrIf0J4lJqC4vGm4wSfnk7x3mMf9DqZN9+/gV6tgwPvzW00j2LY+BbLgCuDkm3YbzIMnNQN9DXWe0ao7vlR4busUDy2iPIMoTJ+eSXySRXRqZIlIS1NnQszRcrw+eoAdvN43jNsa1Q/1CIRueOm2TSCT2dU3bKfa7DU7DsnSyBeUSjFEi5+OsIcRhzjLcXYnnKvJZhvWsoHIWq8H3PbGPKDw6BkwM2KKCFCmqEjPrUaqjbTKM3gfwopFWVXNMJe0ls7LEKCBFoUDTorPXdJ4+850qMqD6dCHKzayMsMK4SiI3PTCigPc9rUrY+TqzyUjdxWhpXHdGJJhS9Hncpb5otYizlstz7OqSdPMc5wpWl0/QtiCSIfSDE2jM2LIhslV5kx5EmDXozEJjrBW6NJfQPr0BSBgitD54AU1ksFTK98oAbpKasBjDrq3xvidGaZbuui4zwcjz2kpkPNbpByc15QyIbBfDLTo+32XuUQHd9Bm8oo419wRtLwTN89ks17dLGf8YJlMlwBUfoI8px1yJGHp8RqzWfYuPUcAsSbQDOx8ph03dd6QUxmtKUeU9wxB9wndhJNE47Oqs1SmwGJUiYWCyRmpyehABCJJuHwSpk1KQNAPikjREu2Rn9kHrh5IS1eHQ5fWQASqjUcopJKVZLtZYa+mC6BIKQjTCrsGHQOkMKZZYDcFqtMpp2Ez5Y0txTJ11OXuhcmYop3Uxcv8ALspzMYnG1uA6DpHiCQuOUm9IX33f8e5GcKhfpYGzR4M6Njcfc6ITeL8ayupkuRv5mS8rlsuJ1IUG44Z0J0evMkl+PBGJvSd4aeSeVYX0m42ISAmzh0lL8WjcBsqjFJRUyR8YmjGH/OAHjneS1KqOAIyB+upIGZPPYUhrJHl8sIvHMTzWB6Vgl987zVmoN98zGh7J80lKaHz5EDU9MIIMBuSYLx8MIEpASoSI6jsScYy6tJn2eg3N6XJS0WeEY0pZ6WIorE8dCXnfkFJBwaBon6LkeiNiXId5Hq8x9wZmc3xyfcPRth3RCthhZg1YLZ8ZEilEjMogn7KUyDZFQhRKD2lRUFmM1Mo60kKj1fceQkCnXjgeAYvoFiZtwFlSiPRtFvGNCRNBuFUNhVGZKs5m6rCU+T0FnDBQz8XgszHhLYfKABNpjNfayPWn40bug5fxyuhiY0xOremRYLtru3HOx+esxc1WFIsz+dtaqrIiaiEyU3pYa4LyTNlLG/knczuUPJ832WycQxTx2BAfZA05RvMpyblHhKhh8ODzHchgwVKKdH07bmgaspFI472qM/x/MLCjNuBwjOmdIf0xIHSzUxSP5ZqU946hhhSCOBBFKTU7aw1vI2OK0ROSFiK1lBHyUSLBSMxlgzTIBIrhSUrIJGIQI5+CRNPx2BokNdJI1wVh0/GRrpVyhTF5i0spR+DicHvlMaJLIyLTMaKNxccoCvbZ2Ygp5HtQgoKhXn+yAo2gc7u+R2UwS4w+76lD9CUGF12QtKbznsIOUDMtte+mp+16nDN0PmuyKsbWMhTYwkjjvnXj/hVjRv0PUd6wF+m8p8co62Tca7MRJIw1zqTUGxy233f8AGCMNG6ngaFDKZIZGjMjMKAcpbkXjnt5ItG0HSolFvMZT5+e8/hqCdQ8eXrGj3/8Eb/65efZRnWZqDqQQo/SjhA1L18KaGMxL1mvFsyqUgimkShoyu2p8hmFFAldS/CeHnBFge+ayZk9uMQx7ZlvjnxTpmFLzo3P8na57jTJNU4jxam2XXrr80kW6LFsPaY8ozr+GxiRb8fHlCiD59dprbHOnJoLdUSmDunK6Y9ioDuIpOAJQVogppuW9Ex5Bv1XDScR8/hZaphnGMzWYOC01mPrCGYSGYzqD+kkijUc2xkk4TwNBROHzY6L9ZxPnj5lXpaQ4P76jrZuaNtIwmOVplSJYl6KERhIGlXClSJWWxQOiyb6QL2raQ89ofPoGCkOHYWLuFJutT4lmh76ZGhcKQS9xlIWEv0ZawgqgEoibzNkCBKEvNlXuedpHz1oixoItcd7BJTWuEKEV8n3kveiqm5UhSkqjKnGTEwiCul2UQjFHQisXsXctmNRueaqVcn64n2sAnP7BR2W9v4r3PmPMEaUD2xpiIVs0jFIutIaQ1SKvpeIQDYt4fDUIfDNy2vut3te320pCsduf+TmVUplAnUjjfY5gm4zmlRrjbHSM2atENT3fZ+tgXyGc25cJ0NDvkSmYhybTKAOQ/ZDBn4weGIcj4AQcYREnSPGSNs2dGNKV5yT44Yc6LoWknkjYvIpEAigTa7vy4x6An3spSFcW1z0aFVI6URZOh+IscXrfqSk830kpUyoHTvatuP6+g7jDElB2/QUheLsbC7izRG6NhCTpNQLJwY/xshuvwdEgQLVo00iROnZizmYMEnjlCMaqf8dtwzFfCZ9vkUh+2vfe+7vtngfCSEhihVSh729u0MbSCpwppZUZcHjx49QSoSEjXEkNL0AooU1K2awEomlnlOVFavlQtCfIdC3KWciDNbmbJbSGWQ0LCy5Y1Te5wBC5h6WjAIUfx01wVEVe8w5IFEWQ5SUJHKLgQGoenw8cTh0tK2wCiQVCKmlPlyzWFqePLvg57/4FS9eveLf/Juf8+HH71NWlXjS1tL1ns+/+Iy6uWd1VnCoN7x4+S1/+qd/xnvvr5nNLSn2w0xKui9EvO/Zb/cj0lEbzevX9yeoz4Fwd/QSx4Ee/s7RzTimQ4F2MCanAJghxTeE6eO7BqMx/I1M4+CwDHFPGi4h/zdEdEMUd/SAhuhOTz/gOF0cjeiYBp0+P6SRcgN2UlLkjyHXQodrjBEVUjbO6nSM3rZMOEavw3cMgIipMZ9eg7xPnQTGjBmB088PdYuuSlbLikVZCnvOWeTgHHXT0vSy/qSgL6AF8RsEfid0VF422wihD9T7hq7u8L2QIKjc34SpJZVPIuZNThyFiImBttUiVhoMWke0Bodwn5mRe1LAPOS0vNYaHyPhAW3akG5krHvlDSC30BgrwBZniyPzCMf5HVpMBjCDyp9DThPq5DHFjGr9hOrqU+g8QWWDriQVZbTFkEi9JiQhIijc0HOlpb6kFW3d8u3LV3z57Qt++dkX7A41h7rFOkfbdSfzOkSnyZgxRe7H2uExY2BydDekOIdoryiKE3T04KjGHDpN19PQkhNiRJMBONmdEuo5e+xn1GpM308dTGFHsvm1OSJOiYdLfgCkyD4Yc0uYNK0PUV5Ao5OkXsnKEVp5kgFNTjPHSNcL6lZpi/fQ9kIn6LKEFIAyClsYfAh0nQeTs3A5vTlmYuJwb4PvhVs0JTWQfGG1QUVpoJ+i0I9X5bHG4RYFh0ND33tSlHY0o8U5NrlPMKVeomSVy0VEnCvQOuUfS4qKrotEn1siUkCrhLWa5aJitVpwtl5RugKr7ZGQM+89EmYEMYD55xg0jBnhkz5aH4Lc3+94vHuzfIqIfnEcN/KYvQupPwRZFEmALglhPpD3wna7p673pBTwoaXt92x3ntlC8/jZGp86vv7mObuDbDznl0vmC4s2wrjwy8/+gt3hlvVFyaubF3z1beRf/rf/DX8rfsLl5ZwYm3H3jCkSvKdrO5r6IGnUDPR4fb3NnsjxuiQvPwAV8qjmn6F28TDakxrCpKAdGV+fywsMib2UJhv+aAjz96jhsSGCGl92TNcwnfCcThgzljEXmB7cpQ/qiFNDOH0sZNLuEz7NNPlXGmqrx0j1+BXqxLiqtzw+NYLTc3ibIR1Hf2L7pt+XEsSmQ/eRpS1Y2hJrNEtr2RWOfV2z2Td0IdL5kCH9MseD42a9HlN/wSe8j7S1NCT7EOmjiNN6El3uXQqTdL21OYWjNTWJwhpKYyjsICNkUFgU4i2rIaWY92pjDJ3v32IEM7tH7hnVyuRI2mKUwrlZVuUoj4Ch0SiEcT2Se6m0UhKlZGOhIxhXUZUV5dPfIex3tHvRpxvQpCb3JwpRQsCHNGYUhiJhBPb1gd9+9S3/8l//BZ/95hu6tpelYnSmvjvOrc2pV4CUjaD0ofnR+ZSe2qEWKenpoe1hMILyusHbj+P7B+OossOBUlILRo8mUGyVEqqwwdBqJWwpD9anc0IR6JxDDWWDcT94uFrl55hBSrne2Al4LUn5p+8iJFk3GhEDtkplIyj9iUKAEel6MYJ98OgoiNgBEGSdpDt1K83nMQUGabGUU/QKcSBUVPiB61kp0SvMhB6kRPSR5N7i0CafywkFbXOAJELoJsuupZQkcjcgdwm5ZCXiuMYarFF5uUgquWsDfQ4oFB1VqSmcY72ec75ecn62pipKiqxYkpOuQocH2QAOiPs0zrfK+8iw5oZ10nfdyED1Lsc7GsHEZ5//BbY65P6unMrRakTvpCT4PK0MIQ0WWRjmY4QvfnPD11+94otvP+df/2LG1zdLrJM0at8Ftv6abz6/5qsvrvnll3/OBx895g//6EParmWz2fOf/Rf/DVdPLnj67BEXh5J6v+Gf/lf/lFC85tmzNUp3HO2VADNENiSn3aKAQW5v9xn4IscgzOqDH2HLSVyLvNbV0QgO90KUi5LU4DAh2VsePJbhPsm1sxhSJhaQNKme1PViBt6owZFgaMlIYz1OZiGN1yg3p6SeZcGkk9t0iB6/y/CcLP0o2+93JhDeIbXw0MC+DaL8tijyba8bSq0DyGHq+Z2tV8zKksP2QKxbrDGUzuKUYTmbozPTh1eJGAe4ubCnxBCIg5RMCFikgD5bWOJCjG+yVlj3jRWPvO/YHw65PzIS+3CE7OuAJquqZ2Ly0Au3Y9LiIo70eKrIaV9P2zfUbXcyCSkOKbl8oVpl4IkozBdlReFEaTxkuR1tDD6GTAbdCfBHqdzTaUg4bGlySrrI3n/kw9/7BzT1nvvb1xz2Nb73xBSF4FlJrV9b0ZHUxqIAVzhutne8eH3NP/nP/hn7m1vM5p4/vkyQDD4oXhwimybyhpJbzmKgtciQWUOKourSZRaYkI2Bz1mbYW1I8/iROD6pSNKihDDc06ODiqwbcQPEMAwb5IBUnWYphqMoity6o7Ko8RHFrHXeO059FlIULkuVsvOAwrctKkVBlyspJbQ+cKgDKcp66a2nMA5mc0EtK1GIDzHS+YbGQ9317NqaYKHQEVsVmBKSgX3T0fmAMzYbnogrj319BAXJYJ0wBwUiygoxuzMa1UEKSeRyQjpZg1prPnjvaU5LB6xWWJVw2WkUJ2NgByLPiwy6NNf3xLDBZQJts1xIxJ0i0XdAwFnP7GLJ48crfvLjD3nv/fd4dHmBs45BNEuQLyk7L8ffsv+Fky02ZdJ9CWQk+xdCJET/xr7yXcc7GcGU4Pn1c+YvyBBV8ayNEf2rEBKQ0xnaSU0hBXxoAEFGvXh9zf1uS1CB7WGP3gaU6QDxyJPzYAPJejaHW2abxOv7iv2+YXO/Z1vvWIcZyfbMVpaAZ98G9s2Ou33C6GNKQxyHXCwfgDHZU+h8fzLxA+x2QIUO0zBEagM7jlDDDb6fGg3DAIIRNNPw7jBJZ+aIUo3OzGhUR0LuIa0xDvjRlqYhGhOX6JiWyYZCACenHIDTYzCEbzwuTzLW9CbG5o1XviXV+l3fNf398O+3vfa7XjPt7Zs+assCUzjQ2RdJQ7pLUSgRKg4q5yzS4BBBtEK0HDqVexRtLu5LD+lgdLQTOjplbW5v6DlUJW3bZ+HjNBrBqqyEsqywaHNs/h2dJjWkh5HaYTS4wmKtKIGfXK9WI9hD5T5Yifb0mCYlo0CP6fh885MgeXne5Pdqi8pkwtoMHL9yPq6cERK4qsY0cl2DE6XIkbg+qheE4Nkdar55fs0XXz/nq+d3pKbFekNhrVAXanAanIlM8xJjWpxcuwd0phPTCRJWWEKCjNkwBtNod6gj64Q4lUmMYEwJHXJ0OBg7dcy0HEFox6hhjCxydDhGkTlCnLblHEsjb1n0Q80/HmvaMUtp5Zt1HExtxL0NyeODGM6+60mjbEjKYJqO1ieavqPpWrQToy+peXlNSKDjQKgfQSehdFOy/6SYry0dsRnGaJwRajdBiCoGqsXp/aUgO3eGoJKIOFtHWZQoxLFSg0i5yjyn2TloWy8I2E6UfgonGQDBLkasAecM5xcznjy55NmzCx4/vuBsvaQsXBZONrKn5eyNlNuOkceQ9ZjuB0PvoGT6QgY25ezPOx7vZgRJ/Pzzn7ONLxCik5SZ0ksGZJTOzbuFE9HDlKTuRxIj+M3r17QtVOuSve+IB09kOzZn2gU8+nBNeVZSVJ5O7fnm+jn3tz3bTQfOEaynVVtWjyvKVUG5ntNpz+1+i9V2nMAheht7+UZDAz6nI/KFjYMnr8ntGXlwj+6G/Ay9kdZkHcDc5yPLT2faK3JzdI6YR2aboQbBqefKNEKLxwczLBnI70uj1yX3cM6HJrkxHqbX3naMy/2h0VHqWHR+017+oOO7jN7bnn9b+8cbrz21gdh5iV1UWC2twCbXj4yRepDDCBIwRaGuApQDoR1LRC8SKwImEsFabS0281oWrpA2CyVWdhBc9v4IIjC5Gdg6J2K31tDR0/qW280d+/2BNrPjDLUYpxUYA7aiDy1d33K0FkME4kYuW2lHEuSmlBdyY3bWtYQMgvExA6l6MCbXa0q0KVBK9OKUEhCJTmKsYhIEZMp9ZGpA7yah4II0ApqM1dSt5/OvnvNf/td/zs8/+5KvXjaU1jIvZtTdFoPHElBGU7i3z+NJz2dunldKUxotunrB0ulOBKkH3tls8LRSOCNgJCbGLaU08tiOqgJDW84kXFAy4cDUMOb1lwvRJ4YXxhTpdzpxUaGCKIaYfJ6h94KgDlHI8HVWbXAFvU8c9gEfpT54qKEsHBQGbCLS0bJn3wa2dcdmt8XHnsoXGCvryKcoArdoOh+lDo1Q2pEUISCZjnyOxhisNhSVwxpRouiy0XalRVkDE2csAb4LaCfhdOkqqLQU1lMWTECa0b2P9L3PlGoiZ6WyQk1VWarSEfpOaoMq8vjxOav1gt///R/x40+f8cH7j/nk4w+ZVTMq57BG2iRAknAxpVzOkLg+uyWT+m0+57weBmJ97z0DOOpdN7Mf0CeoUdaIAcworJg9VZHnyMztesjRQsKQckvF+dVlNkqO+brCOIhpCfnm0xZmq8T5VcKYhLEaWzhWF5FqGVmdr5jNFNVcQ3KUc8V8Ba6QG1twD1mWRCmiEpDAANHJglM8ZCQYlsCxKpdTiTnXr1Ump+a4UcdM3m2skQWXJC2cYS0MYJWY4bxDqnQwrr0P2Ts/jdSOtcbBk5nWJCbRYUpo7XLaUDH0V00nXaUjh42AThQQc9/Q8Kp0XE1KH73lt0SO77xOHmwa35f+/N4U6ltOIaXEqxc3tJs9Tknk57RmZt2oYVZYN/aQWWfQ1uDKYmQc6oOn7ToOTc22qcfxdc5htKG0Lnf2JELfj/2PemhJ0NkTTpJ21EaMaUuPj559WwuTTYJElM0yRzRGC4/i2XxG9AF1tIICEzeWI0BGjwZKKU3I0Y7Mt7CFhJgRoEbGQedowBYV2jhImv3unr6X/rwyc4xWtkTlyNVHIYkOPqLdgIIWdzyi+OLbV3z+5bf84//n/4f7m1tS0/D3PyrZtYm7uuf+AD5oubeNlazK5LoeRnWKLO0zXn0SZXpUTi0eyRRUrpvFXEaYRmojsjgDXqbgtpCEli8m6dNjiADV0LZ0jPCO93yOnB7YvO+6F5zWVNqiklx7iBGdDFaBthGly9yrp6j7jpACygSMFRRvsz8Qo8F7TbEw4BKLhePu0OARzVPnRKNvcVaRVI+PNbO5SAn1TUvhHM4pqqrA95L2r8oiK96AdQXaaNygOEKidMVY2Tn4mkN/OF5rTNy8us/jmTjsG/o+0HXSAxmjcOKO6eUQpfSQYs5uaKrKYHVAJenLLJyhnDk+/vgZH3/0Hn/v7/0xT69WnJ/Pmc0KjFaim5iDDD1mOkIm7PDE6HPdcSAJlykUR4XsTCak1eut0/W9x7sZwQS3N3uK0jJo5ymtBLaaG2itseNCirlXJiEF4RQ1TdMDQjXW+Q6VPdLhZhHC/bz4R9BHe3ToUmLnI4d9yIZsqMF58YyjsFJoJQg2qZrkImvK6YMY2d7XR6MDXN/cC2oxe5yD15qyxy29WkhaJtcL+96LuGXwBH/Uv9PaySYVI33ocoEeBqDDoDzR+0xDNtzow/nEI+hIGlw5eq3DkKSBzimrT+S67G6/P7lht9u9GL+TeZwwBZ7c3OqYvfkrGMFxT8m5P3Xy+EMjOO2RUycb0cNDKdgfasZEcYLruy31ocYAhdZYpamsk94/awRlZo0AHAonta2ZxxayIfQxUDcNu/rAoT6MsljOSoqzMCK4mnwUI5jHw1iXoyM7prUH9hztDH3yhBTpQk9ImRVF5bnPDV4J2ah8CPTdBJxF4u72HpJs9Pu5cNzOqgKTazClKygL6X9MUVKORmvRxrMap8PovK3Wog+ZEtzf39C1Lc5ZyrKiLCuq+Zyurdncvub+fkvfdcS+ybRqhqbvxjn9/Kvn/OaLb/iLX/0GmyIzA7NLQ0MgeE/d9XQhERJYp99YO23b8hCdbMYywSTdH0V1I6Sh5y87nPlalVIEYzMX6yRyHUkZ5G/ZKNPYj5Zy9kWcwRFbPa6/o1juwDw1nNXp/THwng7r+ua2wbcJjcHnzEPTdXnXSZlmUIFR1F1N1wfq2lM4j0bTbQJWa+lb7SymUJi55n5Xs9m3NK3HFh7tPM7Lmmm6jqLssSbSNR1loXFO04UW3yXaJgrLkQZ7SKPMmlWgVZQrDKKYE4nsuj2vb3YnTvj9Zp/HF9qmIwSph3ofcp9qz4BtH9jkfIo5ANGZGCDm6CwRo8UYJHL0kbrx3NztadoOhnJTUiMpxDSKC6GVvst4tBNDIiQlCF7YkFTmLz3uXYqvv3n91j3lbYf6Xrj7RE55SDdN1kVeWd+f/jqunAfv/fd1pDQSXwO5AP5v83nv9pTie1/6b3+kQdpJjgEu/t2vf/Bv9R2P/3UcP2C8U+IEBv8QoXrycer4L/X2J4dPfauBfkvh53ueO33mhw7bsFkPxwDEmH7o25yDN+f0+18zvb3Vyf8mY/A9Jz/U2oY+ucEg5Xc+MHpvLqK/LD3+13scMyjfeai3/vnmkDx4wJohBHnL93G6CtPbBvgtw5YDmnFcR3KMyVveNpxqYt7fWDV/yR6QGDAd+eXfsXh+yPp+2703ZA+11pN1+Fc43vFEYpJgZXzblFz3wfHORvBvjr85/ub4m+Nvjr85/kM8vs8IvnNNsJrNR5TWv//jh4SVuT4Z4ygzUtdHELfSQyQ4ccd+6Cn8JeHgm8/+O/Atjo7f8XsmiCjnzL9nL/zfzZFSou8fgH7Um/88WREPMhZD9KLIbPM5AspZ9lHRYUh7M6ShOX7wGyvue8K/twaZf8mh1PEcx6nNKXilVa6VvVk3HVKBvR8Qzg/Tz8dzHq53+BxjJI0PR07IYxRyfN3JraGG9p3viLAePPZdrTJ/lbX5LhH3MIfjdSs11gTf9tqBZWZsAZimRNMkMosyxsOxXC7/e3F/xRjZZ5YZgMVicazRn7zyB+6Pf93Hg8X3cH5jjBwOB97leCcjqLXh//h/+j/z6c9+lzY3ooLCREEVKiYcdOrNdMypShx/aYgeMsvF2BhJ5qBJ49Y1ebmkJSKGMfGQjjdDDJ623vPy68959uiM2DX8H/73/zv2ux0oxcd/6+9SLVcjY4XU7PWx4DoeKisd5NpDHHoPU2ZeAMbfGcSQBhaHrIDOoK4Rj7DuDMxI+drkezVTFfuBqkwYLIb+xHxWA0gp9Nx+8RtSFEaHf/S//I+4vFicrJXpVpAeai09NNaKY1/jeAxCmN83f/k9w+eN03V6Y72ZaBmeV8c1pBLbTc3/5f/6X46GsFhZlFEInacU45XJ2mopZTCMwjqFnclGX+jI01XJo0XJ+0/O6GPg0HW8bgKbQ8e3r7aojPJczUsOm579fYdvlKB7U6LPygnKKbSV+dEhoUKCIFDxlGsk0qcUCX1kqF1LwX/orZI6cNtIukYp+PDZkvmsYrFYEPoOpWA+m/PB+0949vSKv/vHP+P87IzzszMh+w6RGOHl7Te8uHnJ//0//xe8eL3h9c1O1l2KdCGyXhvKSlM4w3YX2O8DZ0vD06cVv/e7a9rOsj8kPv/tnq6O9G3gfn+gLBWrpeXxlcNa8CGwXgnK8IuvWu5uPXe3/dgeIDU5YWrqN8N1Kc7Pz7FOGrXDoBjv/QiYseaoHafzWMi9m2H/JjeZK2kzF/jDkHA8XcB6MO5a8+hiLqCSuaPvenrf47swOkCi+2i5vFzx3rNHnJ8vefpohdEeTU/0e+pDx/VNze0evn214//2n/4LYkysViv+yT/5Jzx58uT774X/AI4/+7M/4x/+w3+I957zszP+8X/6j3n06NHJ3juAmhj+fngMJYr81KlD+u5Wc1qb/N7XDeWR6d42BRcCf/In/4b/1T/6RyellO863skIKgVP33ufDz75EU3IRHBoRE5PQCcpDUtTnazNONngjx/4XVeX3xMz4dHgtSJQ5JQNy8lmnm+GAQd68hXZCDb7LX2949GjC0J3OOGqdFVFMZ9lmaDsfQ/AHzUFluSJTox9VTFGtD8axONPROdmd/UdRnDwTtPAxclxjMZFl5LA8bM7qrUaZWgScn4mS4aEbsKQoBTLVcXZ2ex4/g/mIKl4tE0TI3Vqs6ZCtTKb8vAkcppMZgJUyr1HY6SQMqJ/uIny574RLk1uMjVEIdLacuqLyJPDeksMEHVxnEZYuwbrFK5QzBwslobl0nB+XtCHgO3g4Dp6Y6kai3EWa0ULMHSRvjZYZTLpQiCmjPEtjRhBpUidAJ9ijIOtJKXjmpngjR+e/sljAFXpOFvNeXp1wWG/p+9FdaLrew5Nw/12L0ZDS4O1s5aL8zOWyw94/GjFb7+95fPlCyr3kq7v8D5y8IH5AopCU5SGiAflWZ1bVucVy3XJzDvKEjZ3sDcdte6oo2K1cjy9qnh0VeCcREJFIfdYceNxdcQWBjN26iRQieih3xyvS5r9BXQ0AL2mfXnGHregYUSG6FeIvzPq11kGRuKQ4rg+JaKVFpl5Je0vMUY+/uiKi/MlH7z3CO9bvG857O5p25669hhnmc1Krq4uePbsCedna64eXQoBgvJEv2G3O7D89pbi9Z7an9bb33//fd5//33+Qz9ubm7Gv7U2vP/++zy5ujrZy4/R9ZuG8KSNZJI1+KHHQwP4vYYwHbMW08em73316tU7f/c7p0NdUVKUFTF4acRFC5Q85abdtxACw7DHnsSB44kOzz88DEeLrjhuqkN0KD+TNwzGagqaGCYmBrzvhHPP94SuPx08o6RhdRDTzSc1QrGn15LTwfJ4To1kTsqBlUbQm0MEmEYDOEjbyGdJJJGDvsyDmJuMUxo3C7nJzYgkRZFZVBJk1YBiNhPiWW1PQvCkE9EMg/Ywe/AgIj8OGjmuz6+aXv0R+D6N5kajNv2oCaI1pZQp3iZrIPOiTdP0Iyl7VntADdqMnBwpKVKQNpUUNdamjOiXyQs+IzNJVFH6CKtC4xwYk7BWkbTGJY22YArFbFngCicQ/JgwOsPKC4cPkToFjJNrtKXOjCzSPxeCECCHLjHeAnk9xGz8hTl0iPgjZqLhJq9QlGXF5fmKn/7oKa9e3XB7v+ezL1+xbz3fvrrn+vqe1aLkfFlxsZ7z6OKcP/7D3+f9957yyQcfEXrHn//qN/zr2S+422yo255NG0B7lEkUM4VWnsJ6Hj1yrFYOKJgXBQWKJ+eOmyi6iHVSXD4q+PTTNWfnDuekBWNfe3aHHmUPaKdxVVaUAIgKV0D0mu3LIxuOzoi/QZR6uA/ediQGUgBx+IxRVIVlPnPMZy7rfApLjNIRpYXCqywLqqrk2eNzQgzU9YE//tuf8uGHz/j7f/cPQR2I4cD1q19xfbPn5Ys9UZWUszlPnzzl8tEVy+UZq+UzITFQkRjvub+7RVVfsOdLqrvdBLn45vFXSY2mN3byv77jbY6XPP72yG5suuf0nodJhu7Bz9siwXc5fnh6XM7qZDZU5hsdnKwf8HnvbAT7zMXYhSBUlcqgAgiPgTSOf9cxRHUPH/3O1w8RRBq8EWERHz5n/KzjH/Jr6ohMjGAX5NyDz/10ExtgnUKc0ZBvWC1SL2n4iknEFLNRzOlQUhaDzbRjA52c0jk6VnHkT324/o5hejbzuVE0RWnyDr3QR1nrZCxUvmlyu4UIrTqsm4maR5RPOi4K8czfmjk6iU+GRBSkpHPkp0j6wdim43umHyUpk0F/MB0diek1T20pEuG9KUh8HI1jOvT05pKBGz5TEfok/IJDkzQSeZgiq2pohbZQri16boiVptYJlRvT077BGrg8n0tjeoDN6x3Ga1bFjNliTd21dLftmHHQSuF74RntOi9tL1kIlcHoVtKmcdg3xKgyh3UuCkTh3H0YBBsjFF2kjtVMo4Ljej6nDdDUPdd3W+63e54bKJxhXpX8+ssX/NHv/oSPP3jCj95/wvmy4icfXPHFF1/x+nbLb7+5Zlvv8aHHGsPjdUKfJ4pKhIabW81HP3pKaUuq0OD0t2jbMj9f8/RqwdMr4fS1Bi4WBTc3ntevA7uNKEIUM9BG5iJ5zXypSP6UMSaT8uRWnqEl6LgohttUfL6Ec4qzdckHz855dLnid376Ux49vuLRoytS2BHjgdjfsNvdUzcNt5sDypQYW/L06oKqLJjPKq6unrBaLaiqgLZLEks639ClDYf+HpSlrCoW6wWL9ZL5Ys5ibcYItG1LiraknFmWi5LFvOC/70dCEOY+tziMESAIsxKT+zKRyUPyf8Mm+Rbb80Nart41JZq/7eii5/10LEP9u2aMGU4qpgyZRqIRFQcNiSRUqnmHPAHpnkQ2jIP0XXZ6pA+bpE5Gq/+G43QyDNPQZLirUBksMKYRHwyupF4YPZ+jRuD0y4bwO43/VPlkBjszNciD/JJ818NoJo3XNoVBH02STOLY35Rkc0VJKlTp3Ec5yDSFY4P9G0Ojpv88dmYdH5me1eQN+c8hFs9B2ts+drgrGIgCBkN4/OAcvp68b/op6sFzTAzf5IvHz0vHX0GirTAY/AnH4eihao0uDMkoeg1NjFgttGgqiTFYziuCF+ktEzTGWGxVYJ2hjzkST5M6sI/4TJE1BU8oGJu3nbMo3ebMt6Twj+vnzRtc6YTSwo5hsqGbFQ58oo/Sp+Uzg4bSsDu0tF3PejHHasV7z65Yz2cUTx+R2obKOupDj0mRQysyNdYKtVlpspIClpkpmRUzzpeaXVvilcMUFZeXM85Wc/ZtxOhE6TShh+aQ6LvMPTFIi6bJ3w+wc1VpsNZliq5Er+RahCBbflKSOurQIL5alrz/3hkfvP+I3/+Dj3n8+D0eP3pK27wmhA2xN9zeaHb7PaZQoAq0Lbm8XLBaLjg/X3O2vqSqSlxp0KYgoXHlgqL0FGUDylAUTojRrUZbhc5ZheHGHjTwCif8tH/dRzr5Sz24p99iFBSQJn25D3fHv0J0OvZXDkaQ47Z44k/ncs2bV/CXf+d3EWj8EAM4fNvw+mEPnvaAv+vx7jOrDcIBlRiAMQmpEnmUqFOPIep0YqYB9XEDPh6ng5Y4gmKO06rG3/LroSUddqIH7nX+/pQYU5WjYRpfJp6fMsMnJbIIyhvnRgiQhvqkePgMP9lgTRnupwbh4dKIyWeBy+HKBorYgd1C3htjPwZSQtYdRKcrSh2wq6VWFP2phM2x+JRnYzyVh1TZ6viTHZhjljLPWV7vb6RGxrTl1KSq46IcV4IeTuX4mnHsT03xif90ci75ncM1hjxSSoEBVxpsYShmZSZcEUZ77Qw4R0PE9x61b5lVhkU1R0XFTDsuzs7Y3jc0Xc+5WzKbzZjbkq9vbgl9iysdwjnsaeuBQg0UuSZmEyItp5mVBbN5ibWGzUaNEeA0On6wAuUwnmQ9yUaCVSSnOVuVnJmSpCybw14koAKkqGi6QH2zo/z112w2B5aLiouzBevljI+fXXG+nOOc5bPfwqvrW7568YKQOtCejz8453yx4r2rCwoP4Hl8UWLWS67wfPDkgsKWOFNwf1B430OIhB66Q8K3g6J6Ipo8mzFSHyB0x5ZzrRXPnp0xKx1KJe630gS+2WqKwlE4S1lUdL3n0HQYrVkuSj784JK/83c+5nd+9j4/+cmPmC8vmM3OePnilq4z9F1B2c7oY8IWPdo4jC1ZzBes1mvOHz3i8dVT0VlUImbsg8cVDlcYisKQklDMkSI+tHTe4H0pWh4x0tf39M2OFHoqp5mVf/1GkHFdvGlIHj4y0An8u8brxzSQ80/u9WEbmDrVDzI0x3ghHZ//gccAGHvIdzz9rIcGcnovpbc89i7HD4gEh/1+khYbAhA12R7HYGmaC+MYLr8lYhlfmcY350DgSHMkArRvO7H8O6cDTz5/CEISowTLQ7RQCpGUvdAp+W1Q8SRWkiiAY/QXM1R8QiMko69QueZ3DCbzSalBfzChwiSFOPwebJFSGGdGgzR4Z97nlFoWF5WXSxI29B3Ti09Ki45d9hZPfJA8P5NllhfOQK+GEBGnbDDzDRBJotP20DCNNdmjLxKz/FLSCj2Q16XpSZy2A5BttiJNVL8F9XsypWNUmRCFa01ZWozTGCvyKyApaSHMTtTbRDDgjKKIkZktWJRnrKt7FLByM0JWJliv1hjxmoi+xfuO3g+SWYbQeYh6zDBALic7LfyxVhOiJ3Z+JPvVetC2O94ncu8MGwZcXjkWF4lab9h1Qka8Pne4UqIcdyu6c73v2R08PmutbZuaF3eKP//sSx6dL3l8vuKjx+dcrFecnZ/x5OqSm/sNv/zyK765ecnrzS2Pnp3z5GLJR0/P2DWJ1nuaIGoDlXM8ebrEYAmdwvczmmipO48PB7xX9F3M3x+xTmp3ZaHo+0jfHWfLGM3/6O99wHxmafY1v/3ijvv7FkLAWYc1VtZ5EsOvtaIwsoj6PlDXHfvDlhATbVtTHzb4/kDovYi8pqOvpwefD3FGve/zuMcs9xZwNlEWmlllIGhJQSdP8h2hN/i+zSj0SNe19F1L33UMGnj/ro8piPC49z3Ywof9a3zTZF9N0yb56Xp6G+fpuxmlkfJykoUTrcNBaSbft7nlTOzAm5Hdd9X53hblTV/7Xc9P/z4au5THZkDZT4Knv5Z0KHlzywvvGNvlNonhwbccagjZRxs3feGp8TxZa0PKb7A6b//0bJgeuCVq8p15Ut/WRzUAVxQiQDoAUwYDpPJ1D3JIpGNklSafOwXryFiocWOXSQG+w5APqFfyElNq0HbLUZYGFRMxDWaPo9QTYtiDPxWRFIM2+ImTCRvfdYyUj+/Ro6p7zJYvxWFx5m/W8HBtjzdzGqcss/kP0aUZBoapETxORZrY6mEQh89RTE/8IRBNKyVqB0YEikMMomUm6CqSj3SHJKhOp4m9xqSCmZ2zLpegEnNb0SqB7c+qJdG39N0BxcBMHyBpFFrGY1hvMVOVqSwCq0RHLQZ/1NVTcl5xdACGCzsZQNbnlmoJjarZ9z14xdmyZDZ32KIghEDX9XSdovMBPHig9T2bQ82X377mcGho65Yn6wXLxZyrZ49Zn63Z7A+4RYH5yuJfKK6eXnB1Pufx1Zz+9YG+FqHWqCPaGJarEhU1rYqUpiAoxT5AjMIFbNCEJOoNSSeUURSFou84YR/RWvGTn5wzrwx3N3C/EW3P3dbitGjbKaNQVhGtpLGdBhUjzaFltzlwf3tPXTY45zjs7kmhQ6k+U3Klo/Ok8krO4JvgO7zK1IkqAQGtE9ZAYSTLo1VCpUAKHdFnIyhFfnzf0vcdfZdT0D8otnj342TTHzfwYzBwfHjcXPJTw1qaGFL+av2Xp+dzrKvlL2SwwompIVToNOB1x3efBg5vMYR/afvDOzw/ZFZO2iImY/ZDqR/f2QjGpBjQfKM9moyTHiSHHj41BDjpOHlq4sFMjdfwuOK48ak09AbFty/DYUNK5mRjUXkDUjGJAck9SvGBMFj0QciMtaisp9HSAycTOp2gycRqNdYHj/DAeJysOEiAHLslxbBONvv8WrKXo5NwmaqoSBisK7Cu4PzyEb331HVNXR+IwUMIpBAhPbguZYm4/B3Ze5uMdcqazUMESP6dbfFbW1sUTGZBje+cNqdMP+s4TEcQzPEM4sm1DwCbkzEHAg8UogeErk6SslaI+rUXIFK5VJRzx9llJdGhUVgVebK+5GJ9xspesCxnFI3lx1cfo1RGG5aGQ9+yvW3w/kDwNY9WK5RuuNvdjddVVoWkYmOi72WjL6wZPeiu96MWZFHakcdST4QNVBaOjRnlo4BFUeCMYes7tk2LCRq30GAkHX55OaPrSupDoFiI3M791ouahtbs65au91zfb7BW8+F7DYv1GmcLLs9K/sfna/74Zz9m3x5YL3pCOnDo7jDbHb5peH2/oQkd0UR+++SauZtRqRlRd2jTo1PkvLJ8cLHg2fkFr+/ueX5zy8HXFIXm8rwixY6pjFsisqlfsO/gmxdbXtz3bA4RHxQLFVgSKfFEBzGjtI2JqPstv/lvP+PbP/+cPz/7c5wFZ8DYyPpixUc/+4ighzUaSEnwt13oaPuWpm4w+g5jFDFKWtxojQ4d7aHnsOtIbStyWJlYP/QdBJ/7hRX77Z7D7sDh0NK2gb5/98ji3+rIW8goGpyicHHmLNWIqs+Ol8pE/kZLm8hw/0xf90OOIVBIkHvAxdEcWmCG5NIYDT74fAGCp9GZ/6763/T7pr/fdj4Pf0+N4NsMYUrSr/qux7snuhPiqou4Qd48s2eQOBZO1UNTOBjPYXIGAzgMxvF3Gj+RY0gxGpbTBB5v/D296EnKgAkk4UHdMCETGX0AfTRYcrbTTVzlyR/Dk3EyQvQSJcYjka/Uqo6GcdB/G85ZjM2bkz5lrxg8GmGfj+hRRSBr01lBIaQYc+P7gwgXS8yKGSqpHOENEbcoTwyjevRzBwN2XGwxPzD4BNPUxzjK47UOYyNRZQ4m5e/hc8Yx1fKtalgNSpyZk4B+IEGYPKaODbxDKixGRALIJIrSUFQaV+hRbzAhN7JTmvP5ipmtmOmCgkD0PfWhpd427LYHbm42aB0wJmBNQakdlS44ZKLtY3diGucLyM3rmTQ4DWOWz1Ek48YVqk6iYDl8VKg+UbcRrURRvZovib0mBM2inKOQ+hVGozrFoVFYDEYZMeSZhP7l7R5tCi7OXjFfLCnKglkJThvW5YxVNcfHEpUUhdtTFD3zBZTKgk30vqGJEILCJk0KmrvbHSokVpXj7OwcpSKbw55636KVpiwcznmMPc3IbHcHjFbs9z1tF/FeBkcrcY1sHs2glWyyRJL31Hvo257YewqbKIygWp1RdIeaoKALkb7uCSYRe6irAxqw2uCbA0orfBDxYa00hJrtpubmZodq9jijiUGI8G1R0DatIB6VYnO3Zbetubs94IPnUD9wxv4tjof3/lCmCd7j25bQe7q6IXQdoe/pm1a0CjMSmuxIaWfRzlIsZriypKhKXFmhrZUfM7R0fYcD/13np+R+MvktOkLUx93r5IXDo0ox3Qamx5RgQ2KTt5/DSWo3HR8d9sJh7NLk8fGxqbF84Lz/ZccPMIIKopLoRA3GRc5WD9HecBLquLkNAfTxFflyx3M8fe4N2ifUCB45bvTDLjM1avH4etIxLTeoWQzew4PLkkjQk7QZP2uUTiIDM9QgeDpEwgIlTjGJKneMxwUKDOrc49WrAT2Tx1EdIz95vWymp6TXcaytee9Babq2yeeTMFZASr3vj+0T0+vCEtMprDspzXEQRKUioslVtMkLs+uQ0mTjHn4fF99gGE9vanF6Uk6VjA4F+QYYPgNQJ2Zh+i3HL40PlmgaP/ToMMQAMdvKam6p5gZXKupGrkHHCFlP72K+ptQFhTLQH+ibxO624e71lrv7Ld++eMV8UbBclZRmRqFLFnZO0++Jvs9sRjJ3g2MUI/S9GMkQxdETRQiV03WS/dBxSDW9uRX1PhEa2G8ilSmYzRbM1xcc7gN9GymXZyjV0UWwM4XuYLsBkww6icp4jJKm/Pb1lkMrenjPnj1jtV5yvpB6W2k0pVlR6BnGlczLDfXM8+jRBlMqdAFt2nGoA9SRx8sFsQ+8eL7B+JKzasZPP3iEDz0vbu64PezQSjMrCoqixzo/JNBIKXF7t8dow3bb07UioK2URAxGJSyJqPJKSDJOMUDrEy2K2HWUNlG5RPKKtrQ02x0eRecjzaZGGYe2AacNvu2JXS/CvSnRekHsoiD2Nbt9x/2mozjcUjlN8i2uW6OLUlTq8w14d7tnv+u4uTmgNGw3LW94Lu9wfF9abnCMQwj0XU97ONBuNvR1w+HuHr87EA419WZD7Hpi2xO9ZAaUMdiqxJQls6sLqvWS2dma+fkFtqxwyyWWrAJ/kmA5QmneaozyOUUd0THrJUZImrFUIrtFDmaSeKLjPp+O35AGp3f6NSP6/ruPmL/j+8Zy/HOyqZ+AaX7AVP0AYIzOKdEcm2XbpPOeeJISm6Q+jwbQjAM4TbMdt79jdHSabR07UfKoJhga1ceBSMe+vpPtXI3h/Sk/4PHQ1mAKJ95i1rMaN+3ByModLWq5g/3NaVZCyv3yg/4boDQxiqRKCEH6CNMxUaiTFoFVAzFkOZgYkUo/4xgMyyX5Hh88d6/bvKnmSDR7jzFEYn/qqfro6JMbxyFnHseVnJLLEaliVCKcyMqkPE/Dghxqm2qIaPM4HtnXRq8jj/vECGY4dSKNZVFJf05Xapp+yviIT6dLNAYZmdAHgpI+JXRmFSkcpasIfeT6VUPdBpSGxVrx+u6O6HsuZ6+4XF5QLS/4kz/7muvXt3z74pWwrERP6wMLaykXC5ra0/eR0lYkf6BvInVXi0yTMcSkRVOtD2NhfvTLEnif61Eqi4HmsXlYPldK8eyjCqym1YkYFN4F7rt7zs/e59mjx5TO8nr7kvv9Kw6bnfTsWsPlrGDhlphOcdgd2G1u2B4id/eGVy+e8+TxF5ydzfjwRyvm5ZpZccbVIbGYlZwvVvzhB3+LLra83L/iun/Bpn/Ni1d79vcNm5sD5XuJfd3zatOywhKt5/nrVzTtnmUFhdWSriw8q6UmestgBUNI/Mm/OmCNoW0ieIOOCqcEUEuAPjtRcbjvoiLIhpIZnAzWRqoy4awj9YrN8y11E2janm+vb/FREZVhdXHGbOZYLhzNYU/vI/tW+mm1MWg6+gCdV6zVHaWB25sty/NHmHLG3W5PSBLR9w20TWCz7VEabu52PyC2ePN40xgm+r6n61uef/OK25ev+eqz39B//RVpc495/Rrb1Ji2JbU1KgZ0iKjBCSOBsSRjUIsZbrmkWK+oPvwx9tFj7E9+zPl7T5mfLVmvF5jMgkWyMJEseug8m5TQXQehxWGxyuBsSUA6tUQp/rRwoQYDqBR60rJx3ByGPeOYHRn25ikSdDCs0z4DJq8fNrEBFT44WuPvsd7AD+K5/mHAmHxyR7uQcnb0NBIgewjDwJyCL442ZXjkmGY8PpbImde8YegThKOafNXoBjw4BTV+zviwOqIfh5cpLag0o3MkptVokMfINkdFx7w8whMajw2leuD7VBpt9cgYE600j2glvVoxRlF/HtOGOXpM+bqz5UsnZy8pttRlIMYkIo05Ak3jJitHwBCyATkZ30SO1IZFNCAXTzfmk3nKO/sD/pzjGJ66etNw7egZJiaL/eF7jp/78FG5hsmcqQepj/xKoxWFk56z6BN9HyXqMOA9tL3n0Iryu9OOeTHnftdwt23Y7BrQEaVgNqtYLOesVwuSP+A9GNVjAYvCKoPOVxByvU9QijkdlOIInjnZAPIYD2nc6WAnoG4CpgAQJhtlEoe24bw0lNUCrYM4TUQUJUYVFE7oyJSWtRZCTx8a2uAhKepYAx2tr1hdBULKaNs7w7KtiGnB2dpirWVmL1gRQVn2uiPqmlq1RK+IXuGs0JYlFbjb7/CxYz43XFwUzBaK8wtLMVO4ow0kJdjcepwRbtVSC5mAMzKCMYHPWP+opGdzmGuJFOW+dFZT2ISzFmscTjl8FKQs9YEQIKCpbUnoPH3bc9ht6PpIE+borFrulCeg8EljdMKrSKwbfL9DFx13hxofpaUidRrfJ+pDQGtFu+/fFpz8oOO0ZuXZ7fZst1t++5svePnNcz7/i18Sv/0avb3n7PaaRd9Q9R0u9uihTjcSW0RBgGtNLBx9VdLN57TbBvXoEamp6esdy8eP0B+9T1mVFKVDKXPMxDz0xhKoEAn7mvr2BjAURYl7/Bis6AVO83bTn8EKnHROfH/Ad0SRjuHBtJ0rfc8HTJ6d2oAHrRXveryTERxiL8+kQpe/a4jahr6VoRVijPAGiqzBXE7yt/L8kE4VctuAiJEmlSYJznzRYw46pxuTfOsANx8m5GQcBgFUpbLBO/UQjJYfYbY4bqpDc/QAgkgxF6iHj50ofRujcdbhXJEVzd3k8zXz2YzlYsFhu6M+HLi7uaFtO/pemDeYqF+/MfZTmqls/GI4GmbyuQ1w/Hz2hFQQUplHcEzoy7WQ64B5Iodk8dEA5+/J40pKGX03gfYMY/pwxafT6B4ZQo712KkRmL5/sNDHz5Xhb0/GxQyFiqRJeRyMiVSVYTZztHWbU8ZCzqyMNGJ3EQ594Ddff0F9aIkBNk2gjhpcgXOKojQ8e3LBe0/PeXq15rl5wfXNjt3dlqUGW1iWs4LWBzrvhYJPKbTJBiImQp+j+qQwNqecp3qIOgMNJtcUY+JP/+SGxaqinFUszzXWwu3djosy4BeWlFoC4EzFsrhCYfE20XX37Hdb2m1D23XUqaGpOxmbXhN0TU3Jk6bAzRSFCfzii1uMtazOCj75pGBezWD3Aavqp1yWjvnZh9ybe87MHSG8pkgHPr56TN+3dH3HV69vmc8Krp5UXH1iWKwVz963aA2vXrb80//Hi1GVornzRJuwSqMLhbWamREkdjvU37VGGY3JBAakSOk0hdMsSsOiTCyrhC1mrJdr3r96n7r4hr2pOegdbVJ0ydB1a+pesdnBYdeKUHa5QKkAKlIpIVbwKVKnUsSNm5Yb/ZqgE61VhKQIAYwXZHQMYJUm7Lu33J0//IhRAHqbzZavvvqWLz7/mv/vf/7/4/nX3/D1r3+L222Y9w2/53e8R88VnoUFZQ2xKMDMQQ3bdkCRCPWeeBfxKbH/9a9pi4r79QWrP/gDlp98wo/+4/+YR8+ecPH4kqJyGCWtTm/ai0TsenbX93zzL/8NNsLi0QWf/if/EbhZxiEcM3OCShjNMmMGiaFkRq4HHp3cNO4N330M5bXxrIZb/o24lVN7MgQok/e8y/ED0qEZ4HEicCqnFsdmvAygHQxzyrn+0aA99OCHhwYk5LDZkjfD4SIVKg51tvzkUGBKefjjdKjlO9XkBXn7PYlKIRuFvJnGCVnzYCAUKqP5jHjo+XmtJforigKXGUKsceMGb63FaENRFvK3MaS+I4Ye6zTei1qGCiEjYONoJ9LAyUqaGLc0RoDHMcgXnDec6eGTo49SEzwawTRmUuLoUTJG9nESoaQh+hy+RIHWwyKTayeP6UBRNI3wRtRvjgDHoCi/UJ3+If/K6RPJIMra8ak4ed9iXmbAQ25ZAYxNuCKiVC9ipyq3h0RZB9YooUXTGu81XYCmCzQh0AZP3XW4sqIoHKvlgtW8YlUVFE8veHK25P3HF2wOnrr33Bw6tk3Hru747POv6XwkpMCQvjFG0u4pZq9Ya6y2E0Oox/Tw9OiTIhko5om68RANrl/Szzxe32GKHraecKc5qJbAgQM7ojmQbEeaJzoTCCpQWQHBVK4gOY2daTpVUweN6QM3mx5l4KAN+npNZWf41w1OzzDa0YYDrkxcPVqzri4IIfDe1Z677R13+x2H+iswnpgil+eWciEp9cWiYLU6ndPCGJzW4xoDsFoLghYoTcKahLWRssztLToyKwOlU6zmPbMiMnMBYzwVHabTFP45IW5YmI7goek0h+4eb2cEt0TZCqs1RVHhQxDlF9MRA/gk5B7eR3zdoecRDPRWE2Luh44SnsYobDL/Nl3pw0YdvGe/P7Db7fnzP/8lv/7sc37969/y+W+/YHO/oe56tkpxbyyeih6kd3V9QSzmNLMlezen1ZatFTBUCJ7DbguhR4WeM22otGbZJdyvfkV88Zzn2x3N7/4O+5/9hKc//SnlfEZZVjnynh4Kax0uaYqD5/kXX2JXL7DvX3L544+ZP74Ee+QoPmIR0gT9P53/Yb9ITKi1kJYTNY7NyRkMvvIY3U1yKWlw1tPksfSWH04Exv+y4wekQ8cdd9gT5QxVzJv4EI3p8cyPFnzyCUNKbNrvMhjC/MhQO51mkwYU6vh544Z9jDCGJ4eocCjUPoT5nxwxG8Icxo777TAHOX+ulMCRtVZYI1GG1YaqKHDWSLpIa0gClrHWYIylcDaH/VGAABpKZ4leQ9IQNUFHQtT0/hh1Dsbj2EPIxPAfrcKYsn2wmEK0Yz1NoY+kAePb1Th8MU0TnWl8zWAIxyb/eBzBFI9zFNTACwMMdbrRX0ljKXeaBRzXz4lhTONVKZkaQjLTV1M4UXEwMWa8VMJZiRCVCmhlsxGUlShDlj9RiTMTArStp+07uuCJRIwVh2ZWVRTW4bSiWs44m894fK7Zt4FDG/j2dse28WwOLS9fXrOrW+re5zECZaSiMQyVVtI0Lmnc4XyOwK1hzQYSQSWSThwOntTDGQWpCcTmgIqRUHv8ATwdverY2Tv0rEPZgCa3aYRIVSqqwnC2LNh3gIGgejwtXUzs2xp0ItSKaqcoVU/3uoFYQLLoUnP5qOLxasmj5RkazXLRcr2Zs9hsePHyQBsOeBrmlcYViaQiRemoytN1aJXCKI3PLTHSA5sd0pQkzWkSzkHhMjGVThQu5sc01gSMDjgdMCmgekPq74i+RinwIVF3kV1oic6ACljrRL3C2lyvjcTkCRF80EQt7VOhj5Qqom1CWYUKeR8KEJXCR7BvjUGOc4d6czN/eMQY6fuezWbL9fUtn332Ob/+7Df85jdfcHN7R9s0BBKtMaDgVkd2ChoT6ZdX+GrJbn7GpphRW8udsyIP5Xs20ZD6FhU6OmNYJ5iHQLzfELb3HCLoGEkKlpeXpHiG0QZtTCaXON6D2hqsthRY6tsNqT5w9+IFi8cXzJdLzMKOAJmEZO00ZKyA+s6ROlYuIoIgefNV0y39GDDJE8cegWkNUF4nLWgTI8hfPh/T452NoJYqUzZacXIzR+Ioy6NRyk5qTkd6te86MVmeg6eQPfuc7B3g+ZByNjpf9ChFJKhL2bCP2enBLo4cm0kdQ54Hg58YCA8nG3KO/kSmRYybdU7Y6suSZ48f4/Ime//6NSH0pLam7zp831Pv97RtS9972q6Tm19rVuszqrLk48eXNIeSvu/pe09ICR8TL27uaTtPHYYwQo0O6Nh6MRB6D7VKBpAMJ9fWJUeb0aEanXswGRcOOcImz9PpHE3Qu8PKHG52LQvYb+9RXYvyLebsHGUd2riJJ3jc5d9wPN6yPoWQN508l4A+ni5RrRXGZnBJNjCreYH3fQYJieEOUajN0ImejlA5FJbF/IK+g6++fMGL188JMXJ+ueLq8QUXqyVnyyXJB65f7zhfOFaLOVePL+lCou0C68U9ppwRtcUBn3/7gl99/hVhzDjntoismjBQmRijiZFMuZaO+pv5SgM9+zqxqzvu7zscBZfvP8Mpj00N223PZrtjs93ROY+3HQez57wyzOeG5IX7Uil4/Mhxvqp49uyCX3y2ZV/3KNuDhWQ7gm3wXhF2jrny9LGl/WbLdt9Q9575szPMas2Hq0CyFcaIQXz29BHJK37y6Ed8e/Oar25eskl36Ngxr3qWlaUrBxdmcHZzqQGNUxL5rSvpa1V4qqJHGzAWRMlP0UXFZudRKnGRKmZlYlEmHtktRdzQ7l/z1TXc7A2f1494vg283kYOyWEKQ+kjq7ljVhiqUomDhKdtd9SdZt84lnONymtlOVfMzhQuI6aJUG8CtU9s24DWGv+dkUUa7w0me9R0DYcYqJuGVy9e86/+1Z/y+W+/4L/+r/47Nrsd+/2eLrTgoDxfUgYoU+SnoeHj1YKz5YLPLz5lX8y4cxV2XmRQnYG6ITU1ndf0fUvwHXVVYbXi1zrySb3jcVfzOy+/JW5u2PzJf8cXd7fMP/6Es5/9jNVsyeb13enllI7q8TmPfvYjLl4/p+1a/Mtr9rMvsIeGJ3/4e0Sr6TX4ybZpGIK9N6NLYKStHLmg34JMjadvOTV6nAYz49A/iAInb3zn452NoIoelUNujXg2m9s79vWOfb1DGUNZzVifX1JUS7RxOYqYGKfp+U9ADWM6VR1/kRJaGLvl+1PCZ8/ncNjhQ4/vOxarJUVZ4qrlg2BoMGoDfPft46KNQVsrKc8x+hMUqc5sH3qIUvIXaK0l/akUi/kMkkOTWSq6np1J7PeJuhGqMxFYTXSHHfQdOgZS8OgUcVqa45UigxzCGO3JxMbT8Rq9n3Ek33phIVpCcPkVR2dkeOPgD4zUaORewqkRfHBjpwQEj4o9rttQpY6Z7bGqok+w8QVR2wlTzTgTp+eoHv6Rb514eiGJhH9gBGNM6CQyOkPkHkJGyAZo2wwAyYoFxiqqRYkzDo2hOfTQRWLtUSpSFIrZ3FIWBmdk+x7ev609PtZEdZMfg9Iq1us51XzJH//+T7i4WDOfz7g77Gn6jt2+pa5bEeFliEAk/TPcDpFT5qIE1I3HJ0hBM7ML1uWCp2cL5rMCjKb2PSF5yiLS60Ag0LSevlf0vaJrI9GLk9JpTaOgDh3KBVwUVhdRNYHZShrWlQ4UJmJTokmaPiSazqP2B3Z7uN1LcFi5krmZUbLApJLl2vGevWS5nHPfb8F1uFVDNevp3d3p3OfWH0dibiNLp1g4Tx97+hTY+kjfa9pkCKogIIw40SehPavOUF2ATceZC1zMPP4qsY2KWhlqM0OVkSpG6BJ9Shz2B1Kw9KWjsBZLQAE+2owUjyLFBCgnvMFKgcWMGZDp/9/GNDXO3Ri1ZDBfkJJR6Hti5hI+1A1391s++9Xn/PIXv+KrL7/m7vZWDEzfUzd7yVy4kscoLrTmR4tzVo+fwPklYXGFNo6FcejSDqE0wVh8URDaRhzAGKjmC+kjJhLqHdvmwCulOQ8163oHf/Gn1Lc37O+3nJ8/5uarL0+Ck6gSqXLox2vmVxe4/Z55H+m+ecX9rmb96BF2vcStZgQl/YNBqTFMkYyd3Htd243MSdYW6JwWP81mTe/34+80nYVs7I4ZsZzhGzibmcQ4JKYvfZfj3dOhoSP5htR1IvvjPTcvvuHVqxe8evUCUxasz8758NMfc/7YUMyGkDc3mAwprQflkETKIJhJTJaQtoIovJ4pyeLqmpq62fP69Uva/Pd7H3zE6uwcVy5lCsZRnjTdP2RhnhzaZiOYkVIDBdxgBMczT0fuUYkQDYW16MUcrSLWKohimIf0nDHg+5qui/Qh0NcHQtsS2payELVta11OoR0ZIKQBPKsWjI32kzEb1sLJ46evickRotTTYnZEjlFgNjvZCMKDdOjg3Krhf8fNQAeP9j2uuWdVdFyWkULP2aHZ+jnROoKyQxzPyP/zMLIcj2moeXodiUSI9uQZGReJelIGU3kP0UPoE/UhEqJkIozRlJVh5mY4XaAx7Hc1sQnEukfn5vrZzOKsUJ4RIz4h7Q99x67p2B52oDTWOq7WZ5wvZlw+ukAXBU+uLlmdrfnq5Uvudzu+eXnNbYJD5vb0WZpmBBNoTQjpjTk91AHrIfWaj56c8eTsnPfPV5RFQdKK2nuiCswrOKRIDIGm8bSNxRg41B6VNFYZGhQmRjZtDS7gtNSTIwmfArMzQWvGGChSxIRE0oYQFb2PqP2B7dZzvWlpZy1VWbJ2c6p0Rsmc1fycp4szPuApu7YjqA7KPV5tOQz3Up5aZYS1xwDLIrIqEosisvWexgduO822U9x1lmRmhAhd15I8WGMozi9o2o593VCmjqdrTVF5ugiNMfS2ws0VZ0XC7RqZr22L94aud9jSUTlNoSEGR0wBrSLKSJ+nKY1sUQkMZjR4Mfd0DgC9t+2p8tr8bEzEFAh9L83uhxrvPd73vL7f8uLlNX/6p3/BX/zFL3jx/CXb+zt5P4nDfgvaYGeaM2N4zxh+dH5J9+RDuqv3wFYUSjNDkexA2RJIZUn0ATugwxUslmdibGJkf9ixq/d8GxLL/XPODjv2P/9T9t98w6vnr2mefsjN7c3JhhwQI6gerZhdXVJYw7zp2N68YK9fcvn++yzef8K8KjJGQRGNHoNhHRFichRN02X1k8B6bVHOjff6GNVN74OBeEINbECT1wy8zglGQo4xdDyWcKZG8V2PdzOCKfH8my9IKfLtl18xr2YoEl/99nN+/atf8qtf/pwueBbrNe998mP+wf/sf86HP/oxTz/6UV4oSqC5uV40kiArxigNFUdrn3pP6Hva/YHNzR37/Z7X16+4vb3h9u6Gm+vXtG1D2x746NOf8OS99/m7/+A/YTZf4MoyGyrR3RtDubdEo4B4aylg0CQvLQzEI0BGaRFRtc6hjdQXfBD1RGUMi/MzYugJfSuK88qyWK3RxjCbzagKS9s0tHUDSWqLxjpcKc3Ks9UZtqzQ1rHtfk5KG5q6zstkiMpkvAZwxXQDFe/qzRnvvaP3hfTlDS0VavgfkPQQVDL8mhLTTaNGnSPChEanSAwt3377nEMVaBaaP3rqmOmONkSe6xmtqWS8o6zUAex0EhE+TIeM6d7JhqMgxHKcO0XWfyw02kWSN0JfVgu3Z4oaI0Sr+BTRTmEKTeFmhH1kf9+yvTmQfIIIRRmwWHy3pzUlJhpehxvRUlRZb1nBQYPRhrKInC8F7RpSpD3sWZaWP/rpR/zuTz7k0LR88fW3/PKXX/L1t6/47bcvcjHD4L20tziLcJ0qg6+74dJpdhFXGgpjeP/qfT569JjVzHCoDxzaGmioXGC+LLi93xPalroOXN90bHaeEAKlM8xLiNHR93CzCXgf0Qo2+45dLXNhS0OhStbmjPqlot913B0CUXXMZpFFmeuEdWJXNLRBE/qC1BcQHevigpIVJSvm4TGFLpjxBLjkVk3nS1HOZ6wrzdr0/O7ljsp5XveW37xSfHWvqVYzfNJ4NE5DCIHd3UbATGWBIZKCp21aNo1mW1fctpYfP91xvvL84cf3aPuYmM75s19cw1ZT+zjqSd7dXjObLymKCpMsmiT1RRUoraylxicOG6Q9IgjrT9NCCJr5TMA9zYM+3IjQ7fm8T/WHhsP1De1mQ3fY09xuhIC7bfn81Wu+ub7hX/3y1zx/fcNmf6A+1GQsPM1uz8wYLkLHT84ueW+2Yv/sU+wHnzB7+h6X5QyljTjLo3+RtUaT4v7mBqJEzpeXZyit6PuGm+sb9nf33DYdOx3YKs8Huxvq189RNy8xn/8CDvWJxYidh4XBXaxJCpr7Dds/+QVdXZMUfO4Dj//gd3jyt/8A+9EVuizopxm3GHMbmma3q7m5vuXrr7/hw48+5uxszaPHF2iT2Q/Tm84gaiDoT9KXLbIW6CDutE7Qk+2GEATLeQ+0lGl4y7tbwXeMBBVKS9W66Xvqusa3LTfXr7m7vWF7f8uhadjtd7Q+8PjZe/R9z2yxoKiWuGI+nnACVAp5ACIxemIKxNhKkdZ7YiuUQc1+z931DdvtlpcvXnB/d8fd/R277Za+7+i6ltfPvyEGzzdf/ZZHV09Yn59jXSkbqBJQRcz1tTc23uPUjedDilidgS/WMpvPMc7hilKK7dZmLr1ICB4KK8jVkS4gp3+cE8DQfC49iArR/kOQij43utsQsUpjXUlZVZRdR1HWNHU7RoFv5Lwnx8nzk6uKOR2qJ0bnaPQyld3EAMKRHYZhpob0whBJAk4J8fHZXDEzoFMkdB1Ra2xQWM6xweLNMjtuYjiHc5h8wel1jAZwmiZQpAfN8jnpNNkMjlFuQuqKWoE1ZIUDadoOTSB0nvbQ544UoY7TQFd3NKqFKKhFY6XXEyMgG8XQfKsJMbGvG/Rmy267k9YgY0ULTyUulnOuLtZ0Tc+3r26IHokE1bGqLSnC0+uPAaKX+nrXtRzaA7d7TdPsafqGYBTWKArlKLTGZvRr30urjpAygEqKuvGZaUiTUsDoRN1qSQGqhA0OdMnCrOibmq7zoBOushQorGrBB/pWWoViVMRdT9seSFHTLwNFqnHxwLxWOCoqW2KM4vamOZlbbQyFVZy5xMU8UDjP5qDQ2uS6vRFHLR4JGYZsjsqGJg78uBhC0hz6iutdS6Tn4qJjXnlcEanmFtc5jCvQRpFikHaR1NH3CkNAR49Jnq5UlMawmDu6qPFRkbys8ajABNBGGG0M6Y35Cm1HtzvQ7g8cbm7otjt237yg29zT7/f0d/cSFfqOeHOLvt9Q3t9S1Xu6rqHtGvoU8CmifGCmFe9rxcxoklK87jvWMbBSibOzFcZZktHHiCiEPIZJ6t0xYYC1Ba0ife+JocOFlpA6ZkQcCoeA9NZtRyJR1c3p/ZV7n421aGOIMbHdbFFeCObr17fsX16ze/maxeUq90ebk71nyDiZfP90Xc/LF6/Y7fbEFJnPS8rKyR6phnv4GPWNCitBKC1j71E+imRogl4potLoshz3i0H8fMi0fieQ6S3HuxlBBevLK9aPnlG9fM2Xv/4Vt69fsb+54bDfolKk7yQ9eXN7Q9M2/OaXP2deFDz7+CdcPvsQdJFrSj2EToiffYtva0Lf0tU7urambfZ0zRbvG9pmz93NHfvNnuffvuZwqLMHJVQ+RYzcfPMFm5uXBNXzs9//Qz7+9CdcPnkfpUoCRtBjisxQ8mZawyAsCRBQURS/Z1XJarVivVrx/ocfUlYVRVHRtR1931Mf9kQfaLyn0PPRkxIYi9RdrLVYo6XRt7A4q/E+EEMixMTmfk/nE21UJG0x5YzlakVC0ftA196Qks8EzG+mRMfi8ds8ngQhOHwojzHUiQOQDc7U3oxx5+SxNHyVyq+I2EKzLCzv/WghjcZ1z7buiKknhQ2lM0RTs539mKgcEY2mF1djrBWm4/9PsiGn15KANKkJJsDHHo2h0GZc6MZoQkqkKAwxVoMtRGHeGDjsG8Ih4JvA4RAIQRGTwteB0EZKUxM6S1m0zGeR2axgNq8E1YlBSMxLrJMewZfXN7y6ueVuk1lElCbEID2h8zkfXD2ichW//fo58VDThRaTncAYJU3+EHKfQiQGRVA93776msPhltt5gdYRrROL9ZqFMThXsCgrau/RaKmFJpEe6trAQQkPblkaKmcx1ufeyiDtZUbB/YpeL5gXVxz2XxH6SDGzlGWFM9C8fk5oI80uoa6g84nXN5Ft3eFj4umTHSoW0JfEb+6gLdHeUZaO69vNSf1MK83cJp7MAo9XHuc8O6W4mMFmpnCloe4ibeylZ1gpjLUic6Q1fdfifU+KkVlhsUWBLpZ8/rLnxW3D4/OOs/WOR2eG+bqk8gq7T5ACvU9stz1Ns8MYya6IBxS50B1zW/LobEabZvTRcegUPir6oOg6WU8p9ES/x9WTPtyUaO/37NvI5tsXbL/6kvbmlu3nXxPv70n1AV3vkMaRyLrroW3pmj1Vf+DGd4T2wH0ItDFSKsOVtfy95YzgCm4S/PrV13x6tmJ2vubxk5/hqoqkFXSB5D2+7fB9S+g7ou4x0eNiZH2o0d4TtjvMy5es7u+4OLzkot1w7nu0tVQKngCxb3jVHoZcnNxgIQrFnzZYW4A23O0PLIuC0ljauy333zzHzwo+eHJBibRVRJ17A1U2SAlWqyVN01CUJb/85a8IMfLJzUe8995THj2+4PLyQrIiWjPlV1bZEIYuEJqWrm6IbT/qr/bGkKxjfmFPcBMqs+LEMU39bsc71wRdteDs8orf+f0/ZL1acfvqBV/8/Oek0NO2NfeHnURyMfD6m685bDb8vxT83h/9HT79nd/nw09+LJ5dV9PuN4Suoa/3MoneU9c7urahbWt8qAmhp+326OwRzmfCtOL7Dq0MMSR6sofoI6+//C0uJQ63t/z0D/8O89UF1epCwCY+PIiUjkfoPKET9vjVcsl8NuPJ4yvOzs5YLVe8evWab7/6hpubW16/vKZrG5RWrJZzlos5P/3Jj6gKhzPSbB91IvZhNDLKOMr5kqKaHVPaCRbnLb2PRGVQJlEfdpxfXFLOF9hyxqFu8CGQej+ypJy0l0wNWEon4b8sJc2IqY05GTkWpBVHva0jcGhKVzel+DvWow2HRpMinDcKF8BGuPc1Pin6ADo8pzIblErU9orGXBCVm6RC83nwXYt0+sXqSAqaH9VOizqEMzhjBFXaK7oMQDFEtFW4YqhTKMqipO17fC9isAly64sjJUXbKWKoaVyL1gVFWaKVI0UBCw3o3T4kOi/UaSFE2q6nD5HeB/q+w2bCYqMNq0XF1aNzArCrG7SR6wghc80+uHxZF5GQEi9vb7jdbnheWpzTFM7yiVtTLGfMlmdctuC9RftrfEwEnYhJU2hNZSylKjFB03YRW1lcIa83KEzKjC2qYRefE2cHUhmg1PQh4nshzD7c99yFFhsjdq5YFBbfG3oPh7rHqIiKPbd1T3Nv2L92JB+53x5OyAFM1+D6REWLUZHSJT4+D1gSn1xGGrXj0Ca2dYDS0vaK5xr6kIFaoSdkQBzJYVyicoqzcyiN4uZ+jnEddfuKD88KXNTs7jWv7wJ13dO2QVh/tKeyMDOBhfM8tokL3bJMjqpweC0gGq9meDWnU44YPMnXxN2XdN0AWIMUAi//5M9ok2L/288Jr76F/ZbZ/R2mqdFdhwktA7B/TeQqJJ7ZwI8L2KTEbwvPKx+4Don1bM5H6xU/O3vGv9xt+Oaw58XNK1ZEVt2B9ukaXZVSVqhb6DzqUKObA3Qty+09LkaKCDOthSu365l3PSl61OWK6nxOlZ5gEA+sUIrkG+avXsK/+Wy4OcUOBhEG3xrFTeX45nLBspyxmFV8+NFH6KdX2CePaXwgNQ1lU2QjqIiDsKMSf2O2mPHpjz/lxctrvv32Bf/vf/Jf8OTpEx5dPeJnP/2U8/WKi/M181mB1VrqiQFSjLR1S9c0NHVD37aCD4kRrw0Yx74Lx3KWM5RVwWK5eKO085cd794ioS1FWXF++YjoeyrnqG9vBaxy2PPy9atM7dUJb1/X8Ztf/AVVWVFYw3peYhT0zYF6e4tva7r9lhCknnGoa/q+pe8aQvLE2NP5hllVibae8ijtUcoj7XhJHlMCnml3W+5evYQIF4/fE+kTZamqGcl7TphXHl6bUhTOcbZes1otubi8YD6bUTjH7n7D69fXfPP1t7x4/oKu7SjLgubiDH+xpm2eYVXCaotWkpcXgd4cGWqJCvVEcUChKKoZPkRaH/ERfIL5coEtS2JSlNWMpmlHXtGpIK2QlB93z7f1CTIBvIx9e2PufvrabCCHZtcHKdKTj0zSPByCIvXCGWFRhBTwQSiwNDts6tH9a4Iq8WpGMGsGyt3jx6rjNYyfP5zP5BoegJoGbw+lJVuZgKgzgheUilnXT+N9BjUMhXQFaIk2rEEo0MzQeN8TgsL7ghg15M0ipWwEQ8QYmauuD/RdR+sDfR9ouo6+73DGMCtLrKvQBuazksJlQz2O+xFo9PAYpqhuW5quY+8VVVEwryoSDm3nFLM1i7JmUbTMjeOQPCkqrFYsjGXlLGemQmtNF4V+3CRFEQ0mKCyaypY4pSG1uffZYEtH6gtS0kQMvoPDXQ+rSOE17twwEOi3tdTVFIFd27GvNfd3lugTu107WTYCpDIpYlWPUgmjYW0jcQ3LEurQ0PSwnyeC7Wh6RUnk0EEfFbboSN4TikBUGmcClfU8WQRmNhHQ7PbCrfvhVc/5zPJk7djvI13jMSpiFTitWNnEqgicV56L0rOyERcO6FRigKgivSlEkshUkortAt4q0accripGdl9+ie0DzW9/i7l7gW4OFO0B6ztsCOjkxxkdwIDLpFiqQG0SRaG4NIbrBOfLGU8WC67mS+zunti36K5H7zaYzR3q7gZVFqJ6XzfQ9ehDg2kOpK4RRYwEZdIUmAxQiSijUcZi7UIyXgpQM1LufU7tAdP0J2jFwYDEGAmVIyxn9JdrDsaRipLubI1fLghlKULLdYN3lmSyETR6NIIpKWJIVLMZxjh6H/niq2+539W8vL4nJcXjy3OePnnE1eU5VVkwr0qhg/SR7XZPc6ipDwfath2FCoKSFpGiacfoc7aoJL07n8n+++428N2NoBgraXR8fPWERxcXPFqf8eFHH/P+hx/hgVevXvLy+XN0WxN8x8uvv4C+5vVXn9Hffk3pHCr2NPsNvmvxzR6PbDIHL0JkKSlMBnuEEGjnHcZqmq6maRv62OKjEBb7FKQXLGmUT2yv7zhsGuomcvH4Ke99+CM++OgjVI4YR67PyTGbLTi/eMSTJ4/48Y8+4Wy1oqkbXr18xWfPX/LZZ79hc7fh+vpWoNdReCqj95KvDp6YLCiDskIi5LJK/dDQabRGGyU0XkpjtGFphoWiCQiDhavW1G1HUVa8vr5GG8vt9Q2prgmhZUC/DkjN02hwwuQDTIjrHlzxsNVO2TvfDhqa5iuHhO/MGVZFwTIUuOixMYCJ1D5w73uSV6jYsbZfopNHh47N4g9JGEgTsbm3Jqen33usd04fjkmorQzSqJtvebmeNGg3KqLXtLUnJY9vDvjQEZOnOovMKs1ybjB9Segi9e5ADBFrLL6viL6AIKTLKUY670mqw0eoZguaPlI3npv7DX3v6XpPCh5rNCkGqtkcpSUFbo1AD7u2B5WZhow+OgTDaGRCBpjQ1YXATM+oyjlPLt/n0eUly0eX2K7HpsgfPH7M833Drg9czCxXleO9WcHjszNJZZmCzgeJFlHoqNFRMbMLQKSmnBNih8X6ET4VdD7xfLOn2/ekg6c/1/RJJMPWTrFEsfmqEFUBE7m79zQ7RdNE1vMVwvF1nEOV0ZjWSd1xAAddLBLnC8/c9eO1t7HHJ0UTYNsamt6wC4FNnbg7JL6571G6Yzlv+V/88Z6rdeTXzyP/6ouSP/+y5PHynvcvGn7/A8U//1PLN6/g85Q4m0XWleKjs57zMvKoCpwVHYaO/lBDcw3KYVNBspdEd0Vfvk/jYX/YE5vIpp+sS+/Z/PN/RhV77M1zysM9JnSY1GNVbkgyVoj1I5gYSCphFcyNQzvLJ4/P6E1Jb0uK9SWmWlLM1/ze/ppz5fm0mvHp2RkfLddcvLrDWiNoVt+hQ8D2XowtBqol2jqMLdBFBcYIitQakkoEvyX2IsukYgkhENsW39W0XX1iAHsSPYlkNOsP38PPCp6ljptXN9zWDV+rxC4F9j6gXt2gbw2mvBF5L61R9kirJdR5ua6vSspqTe8tn/36W+r2N/zZn/6SR5dnvP/sCX/rj36PZ0+v+NlPfkxC0XUdP//V52zu79lsNvR9T4xBsmMhjoHAbD5jNp/xyaefgFKsz89GTcN3Pd5dSgm5MUN2qZUyzBcrHj95D20s28Oe6+vXvPjmK25fv+Kw33HYbXDO4fuO58+/lZYCBaE9EHxP7FuBw6qUq0YapTRBOySetrQt4BM+GZKusEXJwFrnUBRFhbUFs/ka6ypMUTE7u2SxOsfNSnF/4vQaTnYeLi4uuLy8ZDFfcvP6hptX12zv7rm/23B/f89+e6DvPc45VqslCqTGl4u6h8MBY5AajK1E5FIPuoIgQBmVxTqzUoXWY/0rorJSt6LvO9q2pW1bMZbGMqhfjGzrQyF5kjUcruX00o4MPPmR43NHVMkx0lLkiOU0RjneHuLpDT9KG1SOlpwqUCZhbTnCyY0GG/aY9hWm2BDMHHQxWZyKIw/Ew4k5vuIhlqnvImgIMWFKYfAJPgxZXiEFiIq+R8ifI3ShQxcR5xSzZUlZKKpSoWpRMWhqhVEiTBpCEgLs3DPmQ6RuO2l1iJFZ29H1gc5HdoeGtutpu47oA4WzVFWFpwXV0nQtIQqKL/ThOBOKEbE7HlrMYvAS1SdkA+2NpzYtu8OB1XJFVIpiNmOVzvk0fszy7p7d4YBq9iyDwgaLihadDBqpm2oE8TjQ0oU2EEJH19bo2KK1otvUuMU5tlhw/vg9XFmgVCQGT9hH6m2iR1iOVFsSovTksfO4TnFmDIuE0ABOLisqkeXBiFZJhoaNdnJkkFHSR6gMXBWJNkhb0baP7FvFtlWsVjLHi1niw8uOy2WgbuHbO8XNXhOjZl54fvy0wTeG14/gR+vI3CrmFh5VHZU1zEyB1gtUDMR+Rxc7+thz6FvwihQSJs7xneLm7kDoeu72kyUaI+bFN9jU4/Y3qORJRLyGlJmjSrSk91AY68BogjVoV6JsiVmcwWxFqlaY2RylDCTNJ+0VF82Sg4er1ZLL0rBo96g+35m+RYVA6lpCVMIeajSqrVGZ9lEBKe9BxEBoN6S+JfY9MRrJjLU18bChub07ufViEucSrZifr1GFJWnF7fUt9aHBKIe2hr33kvELmUc3cyknpUcMg1A+ahSW3bZhc79Ha0dKkqXZ7RtCCNSHmrJw7HcH1usLrLP4EHh9d892s2G73U5KQWSWm4SzhmpWcXFxwdnZmvlsNtI5vhnufPfx7n2CuUfOZ7JmFRO2KFmenWOKgp95z9XtNRfn53zz5efc391w/fJ5pozS3N7d46zBGgOhz0XnMGSdxFgpMa5oh0KjtZVtMiaSsRircUreoLXBmoLVak1VVSzPz3HFDFuUmErqauV8gXaW1Ie3XpMCVqsVq+UKqy0vX7xgt9lwd31N07S0TUvT9KSUqMqCoihEwVlDVQknaNO0lKUlpTnGWKw1BD3ptdNHLUJjTW7dEAANJCIS2aio2NctbdPQNA0Du8KUWeF7VaK/A/kKw4JIb3mp1F0EHSv7cjZ3k3jx1G4N5NsYi8RhJqu3K+ZGE1KmiAoeF2psHzH9PaCIuuRU6OthNPjAEir1xlL2XQQlytHBWGFiCcLdOSiAxCQ9gzFIRN6HnqrS2EqzurA4A9akzBsL2gjZtTU2K2rIOkwx4EOibaX252Ni1rT4PtD5wKFuJXXZdgQfKJ1jtQp0QWoVbdcRY8RaR+cHwIDwzT2crlGc1x/ZZFRSdMpT07LZ7zhrz/AJqmqGM5rKWmbKsdP3bOuaImhUMKSgBUmYBE3HhHQClfChp+samt09qW9QJNpiz/oRmDPD2aNnOFfk+3QjauxbT++lDUWlguih7xMqWgqgtJoyJLw/vdcGMnx0zBkMSJMU92QpUthEWSTO1wLCCkmxbT1NMBy8xS4MScGi8Dw761jNArs68vTMcntwQl5RRD66rClR7B4pXq4MRQKbEjPdoZKQWzSck0KH7na0fU/XR7wHFcRwGC4IB83mvqbzns1hcv+khL1+gVUB2+1Js4JoDcFZ4qzCOEfVJwyaAo2ZLUjOSgjsKpSrcOdXmNUldnWB0lrk0g473js741E1o+8T87KgUgFT30tmLCWSb0k+M1SpgqQM0TnoW1J7QPtWoMapJ/UefE+sN4S+I/iOJgDeY9oDqjvQHqY6iWn8T2mYrRaU8xnz1ZLzqy31oeb+XtiwmqZlU9fUdctmtxt7AkMmCI9R2s1U3quNmdF1gf8/e38SK1uWpelh325OZ83tXufPPcIjIiMzq7KpKlEiAUIzNZBGGmiggQaCBtRIEDjRRAA105AzAYSgEUENNRA0lARoRkGiVAWSKFZmZWZERuce7v66e681p9mtBmufY2bvPY98yWICLCCP4/q71+zYsdPsvdda//rXv6qqRhsLRbv1cOg5Hvasu5YUMz/44Zd0645M5nDsOY4jo3dLAGGNMGIVGVtZNpsNd0/uuL66ou3aU7DwdxEJ7vd73r55y/3ugVA6Hod+IJR8SIyOqGuun3+ObTrGoefzLx/RpsJYS7feUFe1CEob6dtntMJaVdhrAa0sxlRY26KVRSHqI1kpopbIyJhqiRi1snTrDttYqm2NNgZlDJhG4IiqxgDj7hH9G1GFeX/Jfbx/5LAf+NUvfsl+98g0jrhphCym4/r6mu31FZ+9eM7v//7vcX21pSqtklIK9IcDdV1IGsWYzILZqmgXKumltNRJytQXbybETA5Se7jf73jcHXn39l6i0GOPKwvpaa2YhcbnLL0soOk9lRatRRVj+cx5bu0M5lyOW/odqbJQUVQfpKheXk/AMSSRt1o9ZWXWXOmJN/f3xBhRCeq6praaJk9Y41lXPVX4a3r1gmNtyboGNDrmcw0FuY4Pxm2meo9FqbNFRQU5kVwmVdJBQvaWJqpuSvRDRCmDtqIao61cSwgWjcagmcZA8GCrhh9+ecPVtiW6yKq2GKuYhsA4OfphFOjVWMmX+YjzgbcPe1zw+BjpS4f1yUeqGoxReGowFVWzQk0HKatJCXz60Lj7KLmMkJc0rQGGyTFNnj//2c8JSfH06UuerW+xtfQ6TKZG2Zru9inGGHRlCLYipITbPZJCgJzp2lUxQon7x9cENxH9RNd18sw2K3wcOB7e8ewf/ltsnv+Q7Ysf8tVf/hf4wz3xcUe2BozkeQgREyPKB0JOhCESNPTjdLH+iPi8QK+PznKMinw0PNt6rjvpE5iyiFrP6oUgsHfKihAVulKs1/DFnRC9ugRtlahV5IubSPeH8Cc/HOm6xO06sK7gJy8i+RnEzwPTqJhGxbvfWoKrSKGm2T5FM1GPb5lixMfMqskMbmKYIOV7xmywMfDQBw7Hczw0YQ7vUF1H3tyx/4PfI1xdkzbX3H72gna7oX3zSOM8rQt4ZdhPjl/d3/N4HAlT5osKbtXETdqR+0fCcGB6fEsYjuQQ0CExfp2YSJACOUVIXtIiKaBdANuRTU1sG/LUk/s9qrBsFWBKzfOYI98kzbdJ8zpkVjnxOZHPdSC+57SomMQRKDCMMYZutaLtOlJOvAwR5zzT6NjvDozDxOPjnv3+wDhNHPpBUmcpMDcfMLrCmgYfEuurFeuvW+7v7/nBFy+FTBg9bhoYXc9X3/yau9tb1psVf/onf4ixduFUaC3deo7HA957yInPXrzg6dNnUPKYwQVCiLjxsq7zd22fLpumygUtgrQK1SCF37Fm0fNMke3VDTF43DQuRrBuW+kFVlUFGpROBMaC0Rmyl0VLGYxppLheKnSKERTartHiRajyY5sKXWlUUyac1qAtsyChilGgO6U+VHDLmXdv3oIyvH37lmkY8cGTYih9zAybqzW3d1c8e/GEZ89uubraYI0pOdKALYWfs1B2YapIFLXktM47ZlFijXmy5+I9JZwPOCeQqJumxQBKVAhzTvDsqZxfzMXLioRWsbwzw6nnH8sXRzhFfSWRDwJ7FqObM5giuxWVYTJrzLpju8pEUzOOA8NxX2rwivFVmtooVnFHDgY3NPj2jqwblKnEaZjPpYSe6uz6hHpzCa/VRlNVGmMNq1ZTV2JwZiky0xhsJWPGDXJQW4toQs5KSiRsJllpehujoBt1W7G+akk+YLMipcAwjgzTxDQFkgZtErYfcS7gnGeYPD4EITi5CETud0eaRlFVhnbdQungIVqm5elnPih5maO/nPJSPiHEgkTKiv1+z+PjjvvHR242V5imwa5usM0OWw+kEEp9owzInPKpaDgnnB+FYKaFRKOqispqVtsbmrZlvd2KcTMV2VYlmmzBrtDWUVUTSgn1PEQhmikiZobdjaaqLVUuSMMy1gyJjEuW+0GctT40oEeMcaxbJ1M2ZYwShnUuYhdKQW0h1xnazGYlw6V2GWulq8mqAaUDm1bqO7sGQGP1LK0n89MYRd9poq1IYYVd36CzR6dbVMzYOJByQCuZN4dpolWWWiVBBEr51Ol5RXJdk548I33xI9LdHXp7i335lHrdoca/ht2R2E+89oE3o+Mvj5P0LAyR+wBf7B/4orZshwfS1DMeH0jOkVPCzPqyWZ5fzhFyIGcv9z5EsglkXZF8RXajNN/NgVkI0WYZA68S/EZZfq0b3hlFFwN9mGhzZHgPJFuahZdUuyrzGE1JGUgJUm01tdG4tWez6hhutkzO00+TdLdIIg6hlUYbCWimSaT/Hh5qptHygx88p2tbmtpyODxSV5anT2+5udqyWnVcXW2oqkpUtRQlRWRYdRXOC6FytV5TVZa+7xmGgd1uj3OON6/f8Knbp5dIVBVN27BmU/TxMiapgkOXav3CyptvnDYWY6Xxpy6kEF08w9kiaZUKbBeK/Zg7VZW8DJxIgkpeE1xOGJhZCXQScDJGlQKlJYJMqhii0gliGR5lIOfMb371KzKa3e5Q6puE4amtoWprXrx8zosXT/nRl5/z/MUtq1UnDXJTJIZAXUlRLnMInsUIpChlGaKyrktfxWIKM0vxbUyyqPgwG0BX4IZRlOXLcS62szTabLje30ergFaFbMRJt/KjB1qAU+SeMhvCAtEhORuDFKpmVdHba+x1x9PP1myfPLK7f8NXv/wrQQVSJORMZUSObDu+BX9kHI9E9fuk+gaaqyIvkOaHsZyOynkpo9P6RKZRQFtkzrpWs90aof+riI+BkDNtUxG8oq4T92kgRFFISUpg0of7kcYaifaCwI8uekyj6bY1ta5xx8ywczwe94yjpx89GNBGE/OBcfSMQ+DYO+lJFyLOSZR37B2rVtN2Fe3mjowixkzwAhGZio/DNWf1KXlpNFuk81Lm8WHH67dv+fq7b/ns8x/TtFe0VVfKjQaC9wLrLs7YnHmTuqlp2NE0Uu+66lqZi9ayvXtBu9qw2W4Zp0lKQQAXM71X5HqDWSVW00R0AzFEJjcVJjRURgaW0TWrqzW59fOolHGlLDErBlezmzTHaPluuCaqA0ofebZ1GJ2xNmN1xiqBbpVNAm+2ClpFXinMXSZnhT5mbJbFyxrYdFKPF1INSpFzxdxrDwNVA9pmVleGHFtUvsZuXkBIxNhjfSTEgDaBqsrYKnJIEyuTWGtJBYV0SbpPKpPWa/KXPyL9gz9BPXtOfXVD+2JDUyv4xS8Irifcv+Wvdo5fjY5/ejhymMTRXk/f8Ed4egI/DTtMdIzeFx1lMFoMhwy8AilTDDsFGo0TWXmyK47nWUcITSYqcAp+juEvqw0/r684KEvtJn5zeOQmZsZ4WatzbgSXJXeuUs8JQ8RWiq6uuVrNylDibMWUcaVGL5OK4RL+QIyw3x0Yxh1v3lQMg+L3f/oFL14858Xz5+x2D5ATXdfSNRVNVbFZr8R2FARvJgOuVjU+JLyP1HVNyonD/sDbt2/5zW++YhgGfvnLX364bn7P9slGsG1aNqsNVVMuHEWtjGSIci7GSAxQLMtbzCcjOQORp/ijwFOYsthK9b80QzhbnFUhT6i5bW+U+q1yDF1q4QxVQfkkjMmq1Mkl0HlupXOZYwM47A+g7KIBqLWiayvW647tds2zuxtur6+LUPYMW508+appmJvippRIMeGDsAWlE7GQYow5ifPmYgUTMLrAMHiOw8T9/Y7H3Z79bs/Y94RSG/NBUdnZLcpA2zZoVXN4O0eKGUPA4OfbuDgTH25nsWBGJMPOntPcpWL+Umul6Pvt0fH1Q6YxiZ88v+NpVaPDxHevv+Fw3HM4DrgygOvuisoFNsdXuFcHJrViXL/Erm+w7Vo8xZkAVM5oVrqxZ0YQBdc3DV2rMSnS2IixIuAbE7iQyOME0WCUIWWJsL2P8twV1CrR1ob12sAkB+1Ug4+Zx/3EprP0/cR+N/Lu8YFpSvhopM2OBiZH8JkQIJQ8TaJEb1kRU8LHjA2S+9WFjk4p7bC11OmRYMKdbn2QBRsW/7AI1QMJYvI8vHvLz372F9ze3vLi2Uu+fCGktKqqWG82NKs1q82WFD1+3BPCEaOyQNt6Q1W32KqhUlby11VDs7nCNh3UK6pmLSiHyYx+YLd/4PD4DtcfCGFimkaCm0hhEk3OrqGzDaZqaNottm1Ih/4CoNC2YqTi1ZjZJcWkGkL3BW/ijuq458fjwKoJNHWe11NikshQGWi1SNTFoNCTMEmtzqgoZUNFVas4eOdhTcHXs1rKk67vAtiMai1qe03KW8L+RyT3DWH6jvTL/5jD48RhiAyDPOdKWSG4KLOY9gwcIlz3PdvXv+Xqr3+GenyguXuGfWsIOMJ/+p9w/PY199+95v+TNnxXr9hf35G6FTlGfvP6O+LxwH7YcasH1iqTtca3G7Kt0LZa5oSeRiGz+CgOErNB8MtaoMhlzkhQYcgMWvGgDP/cbjnevaR7+jmbqqbf7/jtV7/mF48TPrgL4x5TIpYepnoOHlQWNaLS6ED0VBMUFSStNEoLa75eFKBFqHxZe7TC15pNZ/mHv/8jfvrjL/gHf/ATrrYbNpsNN1dS4qC1xuqCjCtETawY93kZ0lpR1RZtJbURU+TN27e8fvWaV9+9wjnP7nH/scXuo9vfok5Q+uMZpZl77EmtkXgocxSWShSXSm0WKaKy/JwDXcsiW7oYZDWLOC+msKzaZc8zOO9S43KRaOb9bsnzgvphrum0BR+WXMTM4lytWq6vNtzcXLPdblivWuqqWpKuc9Pb5bykZ9NyNillUiFH5CzdIaw5QX+ZTFKicjKMnuMwcjiMHI5HjseeYSiq8DMVeLmafLp3pZpdK0VVVR/IOmkVMTMcqrj493I7x65kgIs9nAFcBXMvPJWhtM3yMbEfPW92mRfXW2plWV/dsOr3hBQ4jgPKKJQ1NOstenLkENkPO0LqicoUVRcrBqY4J3PWdC7LEkj39ABn3UFxiOVZJCXRlg+ZjMiH4dVSYxlDFOk6rWisoq0UXVMIScpgi/zS6By1yYyTY5gECnUuE2IleUgNiShjtjSBXu7JxUCbb/jJm9ZGtFeNEahUJUC5C4itgCnMoIHo7M5vZJyfeHh4x3evvkFrzbObWxK5dGSvabs1q+0NIXrJDU4HyRGRMVVb5q0mYxb9Wl3VKGPI2giJzFa46HF+wrkRNw0E7zBVhQ4VOiesSjRdQ7tq0bbB2IZ6tUYZgx7dYgOVAltZsJohZ8aY8abB1GsmIruQ6L2hMommjsXbFw6Pmn9URudMjgnrIyiFzbnA5nnJdZeZV54NTLmUIylVulll6jpBE1CrETZOhCqal+SwQfc38PU/J+VHvB+IJfIzVYUxRTbvlIZnShAmh3q4p/r2a/TQUw09uVX4NBG/+4b923e8enjg28byYFt0t5JUUIr4x4aHXlOFyEEHKi19F5PSJG3IRrgEy6pYauTE0S8ISk4stOhlHKVimBJjNuyM5q3tUO2aanNF2zYEMmG15mFv8e9xBSSGOzm9cixOPXE5V96SAZuZI8csJUvnA2DeU0FtDTdXW9arDqU0dzdCamwqQ65POqAgEmmzHmiePY8FKprF6OW6U0xMTlj1zjlikGDkU7e/RWd5SEkRo1h6xbwgyG0rnQZZLIoqkRdlEHOqWzuf9yeYcoY852PM16yXBX/B8wyFgnuKKS8imnLCinyCawXn+2BLyPtKKera0rYNn798zucvX/D5Zy948eIZbdfSNg1Gm9mOLU0cYyiyZikRfJTcnpNi6hACIUaMkaa7s6ivNhoXPT4k7ncHHnc9j7sD3337mr50no7OLwbwNE7VAlNQGKeVMXSrtkz007012mH0VD4137t5wZD/n7sSy4r93qvy5TOTVby/jCJpw8M0cjzucP2B59drfvqDlzzVFev+Aa0DqrKYpuH2yU9wLvL4sKf/qz/DHA7gRmyyGFXhbENCWMel4g9dDIJVZwnuDP3kycpQo0ljRnlwSogw4xTQKFKIxCkwDUFKHsKEqhRVbXh627JaWTZrw2Q0pmrY3t4yjHuG/oDyRhySfqQfHZMD5yJSaJAJ2QsRqjZgEyoFoET9GoxV1JWhriRvnHMUDcpWrqyyFetVg8rwln6521pJpJmh5O5KRGzEi9YaQnbcP77jv/gX/xmvX3/Lum24VYFVs6KqLN32ivXNU3LVkKLj6skLbBYHoOk2DMcd43HHcDigq4ZqfYMPkxjSHNG2Jdc1u8M9Q78nuAPjcETlzPXzL+j8SI6BzgTqpqZuG7wrXXurhnGa8PE8slBsrzq6uiL4Bh8jWdc8uV6RPDxGzXeHFkViXQeJ46yoEJXe3QQl0J4log8jaIU2Cq3j0iZUldYzSksHkdFbHoc1CcO6g00z0VUeW3uUfQTtobrD1BFz949B/QGuH3B//s9w6Ze4UaJZXdfY6yfYY489zkiUWIM+ZsbdntwfqN+9wbQtbDc4rYgpkt+85vWU+MugeX27Jd/e8eXnL0CD94HD6Bi845fHI6/igCFzk6X7fCoPX2dZv+I4oLwH7xbjP//MXIF53ihEO9dkeKcqfqsr3qxvuVlv2K5qVuuWyJbNkye8e/yOfjyNwazAl3I1rRRnGRw5dp4XX06hmoKkZ/N4dmoga/Cy9CjWXcef/tEfSfs6bTAFVUrkOWgv07z0oNHlu9QZOlWaac8tmlJMIkcZRO6tMhWNNXRtx6dun14nqJS44Iky6pgDk9MNWF47GSR19u7HajfU2cJ7/m4ulr9E+AIn5bMlOisuuZ6Xy7qa/5fPnsrZQz3fTxLnmvWqY7tZ8eTuhqd3Nzy5u2ZVokBrtHxfPskKhRDoexG7Ph579ocj0+To+55pFM1DHwLWWuqqYr1dUdU13aojoogp87A7sNsd2O2OeDehlCiNqK5BAukotTuzi2Ekx1rVNdYY2qairi0x+Iv7p9W5EdQnQ5hLLHruPJ5FgpfPonjcal6VpOoMFAFIOuK04je7A++GI+/2D9QmQfLsQkNKFkLF3kjE43NF3DwnmzXTMKD2D6hhwlz36Kaj6rrTZJuNwAwZlq0fpE+bV0b65CkIOeJcJLhETpocMynIQUQMQy1XVlWWpq6oq4rk5bqmsSc6R84BZcXz1VpKQUKRR8tochbYVQrgDbpYauFjyUJkakPbGJpaxAFy8uTkUYjAQ3KKaPSFdKhSsNoI/X+c0mnMl0VeYCK1jL3Hh0dygj//i3/Jj5/c8HS75m7dEpVmcA5dtSjToLtrdJpQOUoOSSmysVKTm6XURNUNkPF+JHspSXp8eEeavJSf5FTylRpTt5AjOTySkif4VGjxmTAemXxgHA7nDjtN1dA0FVprTJxIWokMWoSYDN/saqxyXLWOOkOVwJQ+kcacagi1yphQymAyYDJZUzrEiwPYe8PrY8tfv93yerciJsu6U/zg7sjzq4Evb+6pdERNAxy+BatRx/83KV2Re49N97R2YLuKeLViyNe04Rn21TcXzysDLmceUmYKinrM1NGz9o9UOWBSwPdHjrrjsdlgtlew6og5YJQQt25vr8mHR/b3LceoGFLiOkXMNKC9Q1sjcy9lYnCoVFpA5bMyBoWI2xekZoZEc7Fe9xjemZb1s6ds7264ulqz3kj/1UjGffsrpv3u4sJiEum+nCXvqZT4d6p8nymBzVLiq0AvcLRCFUh6XtfzbMTKMZWRyDEm6Rl5YeDKCp7K51KS61xSTUnWXe8kzRGcRHspZtwUCEHEXD5GPPtd26dHgqVIOutCXFkMDKdBP/8vvycKVaj2pzjk/d8uDeB80PkGAGUinsGj3/Op85dOS9/prfcNsaRrCgzaNWw3K663a662a7abFU0l9YlSOiDnE0MQ9ubk2D/u2e+PvHt45O3bB4Zh5HA4ME3SMNOHgK0sdV1zfb2l7Vo211vQlphhvz9yOAgMarToRJq2pqqkIa73QfTxSKC1GNS6pi2ybl3bYAyMQy/s2IKfGxUwJYqS5yXRey6D8xIsOMEQc02hLgZQqYQqkKSUgEhEaVA4BU7DwzCw7z27e8fNuqOyGhcrQulkMRy9qOYoQ1xdk01DiA+kYYD+SKUUzfYaU8/EqdPTNepk3DPgQ0T5otFa8hUxBilGD9K5PSdV0qjChpS8ZiqEpFK3pA3GFFr1NJGSZ5EuUAX+ynpRqRDPU5Gi/KhcGMpKn3pPakVtDE0lP1qqQCFHrJL+ggTRg30frGk6IwQWX8gwC8Zxmlw5ZVLM9MeBFDO//vWvWRVm8u3NFVEbfEoYNNporF6RQyZHL11LstTtuRQLQpKprXQ78a4nOSHG7Pc7bIbOVILwFAazrm3R7YVIRKdMiIJ2TM4VssJ4MeeqqpKaQ6WoXCSiiVEYtTkq3sSadV3z3AU6yvcYcQRyPjOCGkzMC/1/RgBziXxQiuNU8d1uxZ//9oZXu7UYwUYz+QoXLC+2e5QKVDjycC/GQxmyX8OQsPmRxo6s2sRB11Spw4Qt2rz+wHkOGQ4JHlDYqGmAp25kG0fa7JlCYGg6Dm1HvdlI31GjsJWMmeubLce3K3a2ZkBJejontHfSYDeomRhKKppSqFOnjcQSiMnaukQIqqB0ij2Gva5or65YbTdsNyvW2w1V0+Bz5rumJehzgfrM5Dzj5CTPV0x/zsUxBCqry3DMy/qtdEEyKE5KQaVOazjL8yyXKZB3eW5aCXkrkxeHP2dpSD4La0s3kYTzHj8Ggk/4ScowyDD0I975omomxvxTt082gql4BLK2yOWbcyO13Mm/yQKrxfp9xIT9nW2zUkt+r7egtYaqtrRtXSLAW25vrmjrihyDCGb7opCixOvo+1P+7uvffsfD4443b+65f9gvD2JhbJYBobXi3f0OY42ocZjqrImuGOEvPn/BZrPm9uaG6+srjLVMzom3ZTSrzVoU9ku43zQN11c31LXl8d07/l//9/8HofQ9M8phlbRJUVpf3uu8mLSyGBc1j3zKr1o8OgXRfiweqMoCmykyyjS4BD5ndDOSgsMNA+/uH0nK0tw+kYikajgCKpW+gts13XZD8+SO4XHHsD/w6rffsjoeuPEjt8+eYKsKZQQSjdUpJ6iApq2wtah6zDJcKokm5Sy6kIrDVjUdKSuOoyN5WazfvuuJPmOwqJQXgQCQHJJVmtpa2kZh9YTVicoGfBD4PQcNQaGioq6sRItl2bFGcbttWLeappZSDoLCW9jUNTlBnMBkMQKLrVCgKlUYxDP8z+LzpZgJk0BGc957So6vv/4KbRI733P1/AVP2w03d8+ItiWlyDAOZBcgOnTKjNPIOBx5vd9TdZnbK00LGCI+9BwPE8cIr159zWazpXv2gqkG14+k17/h6skz2m5NoGZVdzTrNf7dK0LyspJkz0VyWimadkW3bqlzQtcNLibG4HCjY3KeX+VbRn3L1Ci2taOzjrvpyFU9sKo82+CpqkhVZZqqVD4pUBGU6OYhXaUUv3x7y5/99or/5Od3NO011tQcoub41YZfvTmwbSdeXu/5/PpAGu4hP6LS16RJk6dMqwZC7fFdIA2OaRx43D9IDex7SXetNAdT8Y2p2d29pDKGH92/4cc+8SRE+qR5UBWPTctPfv+nbJ48Yb1eYa0suYd+YnjY8/rXX9Oj6BGelkIshE7i5GUglll6LnohCLAuqQO1qD2RhSjmlOIRzYPSKKO42m548eIFbbfhcOg5HEewFcmeTEAMkf/8P/0zbq5vS2cHVdI7vujtarYbYWwapQleHFSrTdH0LWUyVYWtqhL9yc8wjnjv6fuBGZIToynr49yntR8GyZ/nzG63Xxj7M8I4NzonI9KVpbzscNgLOucDMSdpFvyJ26dHgnCmZ6wWqKb8ddrOvPjzD1/u8zd/30xCWUSnl+Oqj5YEfM9BPvJdly80dSP5DWuojS1F75BSxHsR5lUnl0u0JCcvOT/vl4cQouzvghdCy3vXopIiZofySsRx55IRazBGIrzD/ij6plkiwLoR6LRpGuq6pus6YgxM48A49FRVTQyR1aqjH/oL/8PogCnMyjnven4LZohQJpMUnuaYMEhZiUkTKgZUEEkoRWk7lRw6J0zVycRLGRt7VIqsTaJKCkeE4Mo9S9hK+u0po4qIbyHUdhVadWR3TWUtRkeIE9kHsi8RyHS8eF7WSp/AZDJVrTA6o5IhOEXwGR9KSULMKCNM4rkfnDWKTdvR2goVM1WRuFJVTYxmcVE1mtoq1p1A4Te3Kx53I+MQ6X3GqIzVidooUpbSe3ymNpptV3N7VbPqKoxtqSuLRtEYSda7wZOTYnShQLXlkRSYT5ikxejrE7SlyIv60JwLiiqwn/ZUe8vb/QPrqxt03XIcRpx3jP2hdDepyQSGpOhDZjcFVjZynVSp6UwYa4ljYBwnhqHH2IreeWJliFbRuyN22pCMIWaF1pbK1HjbiBcfHClM5DBdzDRtLbYS8We0po4ZG0R4wPmAyQbVao5Kk3JgTIEQBno10SbPNk+00dFGxyaNVCbQpEBlszz7LPclK3g4WnZjjY8tN6srmlpE+2MyHEPFr16/xSrP0/URrbygdMmRc4HQdGRKir2ruN9HHsaJsT+Qc166gICgYoeqxpoKZSqIQmJzITCkxCFl+gx9hjErjkOPHddcFd23lBLH41FQDVvhs0YA85lJjMDQpZRMq1M6acYIlshLKWkeXsJGiaQUUWlClmMP/cA4TjjnsVbq68hQtx31We4sxsivf/Ub3m0ez2qyxRAaLZrO+3VLZQ2VsbipCHnEhC4M+Lqti6iJWSJCrTX90OO9FNmL43+qFddGF6gzcuyPpSQi83D/UGrTJTc4Q67WSJojBEfwAe89+8OeGCIximrNfv/4EYPw8e1vlxMsydI5ipMauBO1/WKbY+WzlXmBtT4Rrz3vmn6hn/nJpywnu5RFqA/lqrqupakbrIbKaIyCHBPBB4nCCgMrZyG/iLB3JjoRdJ1hsEyhchfs+tIIlquPJwbcfH7GmEVu7Y227HcHHh92iwH80Y9+SF3XS3/CoZ94++Y1+8MerQ3XN3dcX1/hxkn6bckdRuuA1bPKhb687sypt19C8kYpkHykymBzQsV+ge5cFlae1gYTB0wK1ClIfiJl6rin1orONrSqYkywcz0pOrKpaGyL1gZlreSisiS2bWfouhVXnSV66ZFGGEhBcqGkhD/uL7wta6GuISlN00BlFI0yTGPEjQnnNT4kac6cAzorrI5UGprK8Ox6i1WgQpSosrLouiEGqePLYcQqjaoUV5sVxmo2NzVfffWWx4eBcJyodaLSicYKSShqi5oSjTHcbhpePNlyve3QtmHTDrTasqkSOXiOxx4fAvvhvbFaAkCtT7WsyhZHMKlCCJEFQZPEUFaZ/bQn7iLf3r/i7uln6Krh+PY1x+HI/rDn6vqOum7xceSYNMcAj1MkW+ltmWMEnajqlnR8FNX+8Yiylse+J1SGWBum4w4zHghakaxFIUogvm5JKaByIPmB5PrzqxLFj6oGrajrhpxhlRVTIY2llLBGMxqDVwmdM/sYaXKg1oFt7Fn7Ixt75Dbc09mRTehZVR5rEjaJNkYG3h4M+7Ei65br61vWq5YYIsPQMfkVf/nNa1o78pOn93TWLfB+Ri+tgPpgeHu0fHsfeOdGBrcnI90YzqYP7+qGja2wGOw0SuTmHccYUCkxoDiiGHPm3f09uap49uQZKYniyru3bxmmCVWJoXLZ4LFoM7P9MgYjgiLKlLX3TD+4oEtaKZEwzAmKVFnMipg0PjRMSXPYHzgej/R9j1YVznnImWa1pttsmb2xGBM//9kvaNsVSmm6dlUMmvR3NBraWvgNdV2LuIj3TMOIrSzWGLpVt6x3c414VVUMg2gwxxClYa+W94yey8dORlCVdf/du3dUdUXbtMu9TylRNxalwAeRmJymkd1OIkFpVZZ53D38V18neA4iqmKV9flrs7fy0fDwvzz0+TG9zNkgLs07zyLG8sIMOC+RqZQ2pJOGYjmrpmmp6prkJw6Hoag0wHrdsuoa9KyIkUWCaAYjJh8YJ8/+4ZF+f8QNI9GXheV3IMR5wYHlvKTEUKR+Xr9+s0SFTVPRNA394chmu2G1XnF1sxWCBGB1RYiB3371a37z68hw7MXDK0eu9IQ1o/yVxaNTBb7OJCQFltEhi5hujAQfQVtZFJL0bnTTyOA8WiuuupaGAUPgEBJZWaFz+yRC1NZzjDAEz5vDI53VdLVma9ckbxmoSAFUFultKaNRWG1FAcOLkcil559V4Pt+yYsp4PqqZn1VSxd047AqUikYR4ObwIeq9P4LuN4RpojPiec3a+6uVvzk5S37xyOvXz/Q3q7JWrM/HKirhspUdOuKnCEGcL2j6SzPX3b4qac2mSrVPH+24u624djfE0ImBEWTDdtVyx/++HO+/OFznj255ebuGb/65df8yz//GeswkCIMtWEKmrbWKLU/y6cXWXgDzB0DKogGUpWp1yIqnyL4UfKGKE30IkD817/6K7abW15+9mNevfmOt2+/4xe//Es+++yHrFYbJr/H+4ngJvrpSNaG3759oH26xTQWAd0sKVtyVnz37hV//eorfBywwFPT8bB/gMOB/XikXa1Yr7fYqqM1hif1Dbb2VNUpEgRQptRzzd5/yRut5nzREtGcz3XJC00kggrsY8DEwHdpwmpHY3s2Zk+rR67sA9tmoNaOfsikbNhut1zf3LLuWsZxwlQe5xq+fbXh+cOON48NL689Vkv9YU6JFDTfPlp+fV/xi/uaPjc4VTPSoKzC2lO9atKarz97SZ4mHt+8gcOeOiX2KTLpyC0Qk2afNaPSTGFmjHtiUdypmxqtMymMRANBWULTErQkJ0YUXmmpo1aybMWciMUZF6a2GEzjEzZnqpTp8lxeoHidMjutubm5Y7VaY21VHPvMdrPh9r/1T1h98Qz1Z//5Ul88jUfpjBMz0btFxB8lajpdhxjBqpbIMyZC9MQsEKyP1fIsTz+SW5+76qhJxrspJD+JGgVqszotud7tOgonAVHJSaVUZuzlfeeFnBhDwBrJmyoyIQeEuvdp29/OCOaTAVx+58Po6v1PLoHYGWHmY1b6dJyPH/Cjlv37rP0ciZ79nT92DCWvOR/ph5FcPNMUPdE7KqsK8YFF0iujmVzATR43TQQnEKjAFJKfWEBhNd8jMcozK/M04eXhSyLYE4MieE/0HjdJzdXxeKRpW3Y76cpR1dJIL8bAMB4JITAOw8W1qaIYsxCBsi6kHik+jVNCRbBJkZx4UN5Hqe3TYpxDzLjSSNZqJU1GVSKrTCChiZgMJEnd+5xwSTp6T6PDVooGTZ4sOUQikTgVOajyGdCYZiVRdghSKZ1L+6mc0e8JBaxby/W6ol3XVBoUQeqnFvaZxsSMjgadDBhhtL28aXm6bbmuNbHWVLWhqgxJSS5lrt9bdRbvEy5HmlqM1aqxNKXsoa4yq9ay7WpMrnA+MXkgJLqu4uZ6zZOba8ktP3vG4/2OVWOxRfklaIUDLrRcy7jMSuj/RT8MbTKUGkpTSdSJyeiIiIMvwyfRD0f6/sCxPzKMPfvjnjdvv0Npw6pd4eKRnCIpR/zoiDFj7IrPb1Z0TUNdKbRxaD2B0njv2R0fcX6iMYbtqqbCA5HDYYcPjhg97WoDTYuvr2jrpohpnLaZiKT1STjAlOaPkgLVJwe7lAbMerVyebLg55zo8ZgcmOKEz2tqNTKllpGeRo+McUNG4Lq6qqjqurBXBeobQkXvKo6TpdSDy3CLEGPGeYULhilVeLUi6JZsKkk1n8GhKIVbb/BKc68VOkXqmKhJ3ORMrUR1p0+ZKSXaVvRZx3EsBi3TdVLW5NzAkOGI4l1WjCnjgEOGSYEjLYIMMRWCSJZ62FxyxxZNlaHOmRWlaD7Du5Q55sDNvN4UaDKmSAyepmmw3eXzgojSotajdAIVCvM0gkrLcaCIEJBR9SkFpojMEpXLmpdj2XcZ7Ag6noomKMVJUqjS+SRnsKY5rdGlJC2dlUgYIyV7MRqMlW4yIQScV9TDp4Ocn24Ei5yOLsoBs+e6XP2nbnM+79wYnVlRefkEgX7kAGU/uZFLLd1HokE1f88ZnLkkWss+MQSR9DkMTONEbQ3TOHBctWxWDau2xloj7ZOMSLb5EBknxzh5xn6QWqliPBUn5tOMh5tSF0MxgOns2ua+iTHKA5Ti7oyfHJnM48Pj4lHZuhIiT2WpGxF5rufFPJ4qNQEMHsOEVN4FyJroYew9Y+8Ivcdi6GyFj16kzoIn11p6kWWFzwaXLFmVY6eAtgKLKiyNzrTK4Um4pBgihOyJITFOAYui1or9IYIGl0Vo2rnAcXJURuoc9XUiKUXIUDftco3ZO8J0IvUopXhy1fD8ruN620geK3ve7Y/EnKRr/BSXm9yuahqtePmZ4cvNmrumweEJK0P/Yku3EUamzxVtU9HWltvrDbv9xOAG2ha6BioURtmSexmpVWRt4fp6wxgi+zFgFVxtG57eXfHs6S3Pnz5lc/OUq813tI3GH4aSQ9bsjp77/aUWpYsRZTW60QvzTxuFrg05ymRXJqPrTIUmhYSbKGpEmmkaOR4PPD7esz8eeNw/8urtK3aPOyEu2FTq6yB5j7Ud7+4f+MGz56w2d1xtWtqjoqkS2oj6UvAj+/uRQRm61HBzVWGtKvmsiZQGEjtIG/a1YbPZsM5nGroKrKkkd1bymQuCZNSpw0qB9maSGBhZW2b/Dakbi9ETciakxJCjCHEER+MnLI4dPd5U1JWiqoSg4a2gLErBGGoOU8PDoSHcaCoVicIbwjlp5Iqq0NWWUb/Am5qqMK6r6axURyn09S3BVuzfvSUEqJQQybZKiGNHMm9iZD86fvDyB6zWa96+e4etKuq64tmzp/y1hcfdO175hFeKnQ7c58gxZ94k6HNmzJmw5PqKUEjOF0tujcKiqIBWKxEI0PBV8kzecHcc8E4io7Zr8fsDb1+/xm9ajsfxYs2s6sB6I1CoKuUGMSZBqIxhs5b1sLJFOOX0kE6Oy1kUOG/W2lNu82y/eX2co8FZdnN++OfNyGfbkDhpLocYiTEwjiPeB6ZpYhgGYvSfnD77dHOZz6JBZkN49vZ5FHdh4Fgm+wwFitcn++XvMXTnUdtpj3NjWWjv51/zPgypTl+fiyF8X4LscDiijC11KFK7J8QKucFJViOUqTC1JWcIzuPmPlql3spYTZ1tqW05DYRFS1WLrqOxlrpp2Gy21HWFD5G+7+n7nv3ugHcSAc7iuXm5joxznuADbnIMvSwophQP55QWEWYQ7VCVAypAcJrgYTgmnMt4n6WTgQKfISuRtZM2URL1ppixSrNqWlSjMCrIoqGMcElzIgcHWfqbERXOB4akmUImhcCItDOKNtM0sGoUwzTRD45D77le1zTWINTvObecSBGmlISdG86VhkB5R54gaodeifj62hqyzZgqE5WS5xhEt7XSCtNqdGfQrUSHViXqBCZNGKW4XlmsVdLppmgkzhqZVmWyT6zbFdNK8ZZ7nB8YRrhaW6zNVA10uaZqLH0/MI2iAcvjjmkY0ClgbE1M0uh3Pzh2fbi4LlNptIS2eCeDtq4s2cpznSap19NRYa0makWMkaYVJ0gkNxMu+BINDmROi5dTCZcEsSAmTOpxPvCbV9+ArXj2/I+oV55u7UjZEpPcR+nmohiniA8JbTJKa+oW1jeJL3+i2KwTt6uRjbnm4a06n6Zi2MysJ1zmRBHQn3Pic25LmRIN6FO3FX1GbkvJzssDMOfnBfqKOdBsevwY0ceIDx7npHA/pIjzjsMw8XhMvN1XPOwUU6VxvsZ7Qz9aftPf8Srdsm+eoPIVddKsiwyiH6dlLVFK8fz5M/p+xf6451G/I4wjr8eRIUKTAlPMHKeBAw989/pbnusXvHj5GcYYYor86te/4mG/J9c1v0yBr1OiCoEJKVbvUXhVFFBV0VAuC52QYebICYLSUsKgFKOtaOqKzXrNJgQaFG/efcdqU1M3mlW3IcaIrgzXz+6IFxYg41zPNCm0ClhbS8u6SkqKrDaoXKGSIUcLxXCda8gopZeoTdtTzk8VVS2JZOXZGW2IWaq6jKQ95d/yn7UWpYQvkVJe2vlJc25Zv+rKomrLqutK/bZnmqa/mxKJZVCzyDGXF2aI8MxAnU+CjHQjyPJHzmeTRJ3fvmLESrQk8OV8bE4ex/K9M2wy7/cxY3pO2MlnP6ctxIhGL22YREKqdK7IonqftQZtyMoiqiEQkmhHZrFEmMpQndn/BX5YJNWgqoU63K06bm6vadtGGvbWkjNxkxNY1Acps1hOXThhubz2fndEucZ06XvkhEqR7CFNiThlXJ8IcRYWF58qJiVF3wi7vfhcpRcYoptojLD7UiRkTSoqMjlGUllYY1QED1MyjEGRYmRKiRAzTJGkNU0NLoSiAD9LwpXSX3V6WpIkF8N86dhkVAzgIWqBL3WlabVEr8ZmshEt2pRgKsX0yUCymSR9S1EBjMlUKqG1prV16fiupCtC0SuMPhC0IvpIY2u6JqFUkkLxWITcdUabTFVLX8VpnJgmj3OeFHv8OEISxZmYFaPPDC4yustJWpqeiJPlc1kEisR4SignMgWG2bFKpUelxlpN8iLXF2Nkmka8l8jFGIOtKnyKAvkFj0bgwZAcbx/e0q2vcKUYvuvWGFOjlCVTmkEnRQhpaZwqhkpEqbtNYrONXG0dDXAczpkCLNR5cQR1IZLppWRpiQBm41egMVBni/2FLy1lIohj60Ms+aJE3VW0ytGGQcZwTgXaF5TDhchx0tz3La8eG9rK4EOD8xX9VPNquOEx3jHZWxQNJkMTQ6nfrZaT0Frz4sVzxmnkMEh9b3/sGfZH9m7kMTi883ilCMlx/3hPt17x/LMXKK1IPnE47lFGc3V3hztWTFFgfem/KK3jVC7tAmYIco6YlcJoiazkd6mH1UrIR13bcnN7x8o7nPc87gd8cByOB5Qy1HXF9c01T549k/lwFmm5acBaBTnRNBFjLFVVoxCd3OAtOUIySrS9tcZovXAttBJoWYyVRlk5v1REtkOMiyE0Ji/BiTamIAKnBgx1nahSXqJReY6RlD05R5IKRZLSYkt3ImmGXVHZvwM4NOrCoNKRPHdAOCMsnGX4L+xMng2dmqO3k8Fa8gDzX+WzaU6aX6yAZyzU2VCmonwyR0wLVCADRnQ9Rc7AKmn7kt6zlZvrK5puVaTNxHOpa4nYUmXwdkVWhpANysmDHKIVeSGrsWuLTpkqJ+J59HaRnztNZi36ZqQiNaetpW1XkDV978jZLIoIc+H7qZbQnHDxWU0BxHhfaFpIhUIYIO8h+kCOmUZpVpUsPj6okpcqJSflaC4FfI5475ZSlFpL0jmFhM+GrDRXncZhSVGzd4Ypag7JcHAwhcQUXJENS9DsiMkQR81UlEi+fLEiZPBZ009S7lA3FoUUurpp4mqzYr3uLlwZ7SNMGR8jVdVgs+Va11zXBmymWjXoymLrim8fBnb9xHdvdtQGJhXpbEuMjmrqeXl9TdPU0HS4BDFDCAOEERVG8BOByHG/p7U3XK1aNuuaurOoSnF0A1FJXVZlNUZLvdJuv6dtO5TvObx7wPcD7x569v3ENw8HdqPkHC9GtwFTS8eDmcDUVtJnL2ZN0whNva40wUVmsQ1R8siQxLkJQXE8HhgGKS1Zbbd03RrV74jHwOg80qRR7umvv/kZve/58ic/4enmCc8/+yHPX7xkVD1vxt9SmQqiEscjywJXVw3T6PnuG8eUHM+ejvzxH0Zu152wmc7wGW1lLilEaWeBv8qY1u+1OVNKNDS1OQkRLChB8dIWuBVoUi5CBtCttsQML2Y8GampvA+Ovj9gbObeXfHPX93wF6+ekFNgnBIhaSIWzB2m6bBNKz1BUaRkqevEOEzL4lZVFf/9/+H/gKqq+Oqrr/jmm294fHzk2+9e8XB/z36/5+HhnnEYGIaRr775NQ/7Bx72jwtb8rOXL/jJ7/2Etm148+YV3kvJ1azLrIv4/Cmdoi+gw7ln6SzJqLXcs7btWK1WPH/6DOcdwzDws5//nN3jjuOx50c//pLPX37OH//xH3F1veUv//Ivz4xg4u27N+z2UubQlvtQVfWCBNa2ku/Xltpaketr21JcLzm9UAgr0gbJ0jQNvgiH9FOPj46QItYKcpZCPEuJ5YV3Ulf1wjANpfxBytaEmZ9TEtZ8+Y75OqQe+P5DaPB7tk82gkblpdfWfJazGoA6w4Pf32QBOy3Q7zdSLfLkp78L9Cf7lsFeoj2Z8CdjmWc5nmIZz2g3y6TSRXJJ4AIu8o9KKb78vZ+yvbnFGlsmnsIWY6i1pq6r8vvJYATvS8+sVPq9FbWDReaHs3M5vw/ld61p2wZjDG6ayPqIj7l0UyjVv8ou5zpLlaGKyKwqDXNLNGeqarneOdiNgyJXYKKQTXJSmKqhUhmLUONDyvikixhPybzE0uw3SQ5BZwilU5T3MMZIIgodWimUqRnRTCBF4yrKomx0gdKga8RrdSFxcBGFZq2MiBSXSWCsplWagFzmam253limKV4Qr4zWWGVKxCYF5q2ytK2msYbt0zVVW1N1HbbZ8+bhyOPjUeor3ci2allbTdPV3LaWpqswq0aYeBmGUWSZgk/crlvxrq3BBQg+0VSiDWorW9IDUl+pVUKnQAoTZOmjoshU1rBqW96pQZSmFCgrjWLPtzkS1BqqTlpvCfNd1G+MkR9twWQRqjcmLdJi2QjhYZoGfHTEHFEGYWeamsq0aKwU5Ec15zQYp4Hd/p5f/fpXVD9sqG+ExFFZK5k5k2T6ao2qpFTAVhZXYMKHh0ROnq7pOd69Zdifxr6CJX80C0JopZb80BIZzvmjGerTSnR6tSyrC2mi5BTn3EdGpL10UUSqlSYVZrCUX4iBvFq16HyLUYlKR1qbSa5Zmr/6kAX+zQblIyr2VMYWdaZGjM9ZZKGU4smTJ1xdXbHZbPjss88YhoH7+wf2+z3D0PP4+MgwDPR9j3MBow1dt2a16lit1/zBH/yUrutomloKvWMgxbg48LPRW1IqF1Hfh4Zxvp9VKV/YrDfEJBrGN7e3HI9HxmHkydNn3N7e8Oz5M5pGao/fX6dkviViLPXDxeBAIpTIz2jDVAzU6Bo5r9I7cxaut6Ve0A5WuBcpMrqJkAIxS5SZC8P0YpInmTtDYRQrrYo+c1GHimmBR+f2fMaYgiCKg7zbH/k0E/i36SKB9JLSc58upRbh09lQcTb4TzdVI9qTRYJHnQmozR0LLizj8kHKNRUYNS1Y8bzlc1MzT4wL+SCFzhFNklIHrU6BJzKYf/R7v8+TFy9F4UBeXMoQFphGndnpPH8zywsLbJsv/53vyPJ6Ov9bOk3sHx8JCcYpiCqNXqRPTlFyOYGM5M9EIHBWUlHoqhbGXbGCGQiDJtVQ5QgxkdDYqsHiqbMnKOkI7jxkK3kfi0AV5EiIeVmgQtTEpJiCpveOlBNt3aCtxtiGUYmEWtQJpYI0SbYGjcEqTdfVpJA4usBxEifqKusiDJAhBkxWtErRZ1HHqdqKq61lsuE9I2hlEipQSaOSplaG66bmal3z7LMr2vWKdrMmU1FpwzffvEHliPeJWkNdW6xS3LUVTVdhrxqiMQQU+6MjRwH31530zhyHif0evEu0laauhLijUpZ9Y8KYiMkakhO5NCWwXW0lX6H0o9RmGo0hYdJ7CiRm/lHUnYIsuVkjvxJtYdEZcQcNCm2ldssYMYIpRanHio5EBKNR2qC1xZoGo8QISlcykUB008h+/8Avf/0Lnl694Pbqaan5shilTu08KumxqazF1BVqCsSgGPcJ5wS+HodMcmfjXwkhwlZW6t0KEcbas9ZZ7+XO5V7MhBl9+b4+GUup2Z33l7ozaytiDGgHqiAtMUWuNh2bruFq1RCjI4SJcVihfCBoT5i8FFqnRPSeGMVotU1L23YlyjqxQ5VS3N7e8vz5c54+fYr3nhjj0gPUOcfheGQYBo7HI/f3D0yTYxwnbm9vuL6+5h/9oz9Z6n/nnqFzCoKz+zJ/3zJO3rtvS4Rc9pvvldaz5FnmBz/8oURiXhihVVXRtg2gaJrmchzORCWVySkScyRFSolDJKi8FLnPzQCqqTqVOcTTGmj1Ceqer89HMYaZzNwnsNBPWTBFwU4XfsW8ks5kSDGALE00UioBSIHFfUj042Wpzu/aPtkITrs3DPffEkIg67n1URYsOHPREfzjQeEpOS6kmDNT8p4RvDAi77E/50Rsnm/K2XZhhOZBFB3uuCPHIKyp0qdr3hpb0VU1phJGXC5Gb8lxqtPzOQ+2FhC3vKnU6bqXXNdsMOfXF+BYiCwxRirrsGbEmoGz20meNf2U4OOK85ZNovEp3xOwVU1l9OU5REhZk01N1kEGTJiwJtLowDHJgIkUXUCjqasO5ya8yzwepRN5U1dYndFWs24abqoVykjfPJLHJc+ms6TgSeOBg1EMBaZrqpq2runqDU550JGmsUt+rLKGWiu224a20lLOELMUFPcZ1xi8DxfjySvw5X5MUyaHyJg9qTVYm1h10HaKrjH85PNn3G624COuH8gh8IPrO6oEVUis15qqs9RXldSk5URXG67WG16+uOLq6g7nAu/ePHL/zrHfG7y7pu1q2taSgi2LcWTT1NRGmvXWJuCmA9MBjv2RGCZubzfQGB7SRPSRKYeLzEGFxmQt3d1LhJszoBPKJEy0MupTkmhQK+ooklgpi5j54A487F4x+Z6Iw9aQ8kTwg0jLKSHJKCRHEyZZdKc48ctf/oxnN8+pqpp+2BNCIYLMyKmaxdttMb6KqoZGWzTgRs1Xv/G44TI3fQ5/ziQYW2rPtBalkDkXZJQwRiViLItqyXXppZasjO8iXm5MvXio3k1S/E+i0hqrFKEUXuecsbXcQyFfyfybnGcce5xzDP2RYfQMkxDf6lryTKqc1/mmS9RljFkMyXa7XYSeYxECSCnhi7JUSomqkghztWrPDJ1c2wURUF1+3/vbx/a5AKLn42UwqzO1mxwXp36Ous83YzJGS756fvQaTq+VD6vyZibhgiPHeZjIOq0RGH8m7CznqIWsBqdcsayt89oIJKnRikWzdyaLyK+FHaoEBchF+jAgkb9GGi1b87vv3/n2aUYww7B/pL9/yxR8MRZzDq6go+9FP/Mv8z5iKC5ps3JXSnRXZLwkMkyXx7qg3pZaotmzKANMaXViJZ3BB9FLR+ycRRpKYy6+fn//ThYGrYmzAVKnB7Kc7mwMz/5YjDrvXVe+NITnpjqXaxYx5MRxvy8/B9xYag6TtBDI6uQ05LPvyOV+LSBxTAJpnhuL0sGiMrpAVxnURCARVZJeasbQqKL7nnM5pvSbS1mjZhKMkkWvraWI3xjD4AMpKEIusFZOoA3aaioFV0gDXltZQhTGrVazAK9AJ0YrTNH69DlLR6IkmvXGzn3gLgYLAYXPoGMBGFJi1AEXAz4ZQnLEWImDYSxdY3l6uyY0luwjm7ZBe1H8tFZhjcKoSGNlcrbZsuosIVtWmxY3SYNklS2VVRz7FXVjqWpDCrosEKINao2i0gqVHf1xz8M7j+t3OO+goBFakLsLiU0oXm2EWBqXauYJn2Wyy6ASYooRp0uLurQ8dp1JBFyciCkUUogiRIdXI1oZaelUGRTSDSOr07x1vuc47Ngd7kkpUJpmcnL9hH0acyTO51QiV41CZY3ziXFKF+PQFPhMjGFpNHwG9S3RyxzBqMsokDnqUOoU3QC6SIwZa09RVFnULUbKprPkS+d0hSz44orOeXVtA8YaGu+oraFpA80UGL0vUasp8/FDz/78nOBS4epc2jGlWUs4LdctJI7ZGBnOV5d/le2Do6gZ4ixrx4LYfc96fHacuRROzUZqXvO0Bq0WJO48IJFOFqeJK++/j+EV7u8crOT35/kpAjwZ9szpGeQFgZvFtk/3WOzH30EXiczu9Te8aS39OC41K/O2OIsnwH45ofcHkFLnD3+uOTuplc8X87HtPMyXujrPw+MjvgzYeWA3TbPsOw09RsPT2zVt01BbezYgMr/+q3/J669/jQ8RH/NybSdlF5gZsDNeDyz5imUwzbp/nC53mRDn/1fzb4Kfe+/x04R3jn25lhAi0pxYL+chC0KxVfk0uMgK7wJRXbYaPo4HuslimjVHF5hcYJUctkQZUTXY2tJWlv3hIMr+ykDSkjvSFpQmScaFymjWTcVq1aGNJQ+OUVWMKmKF4oOvItooOjKfrQ1eWxyWwyAeuiWzthLRWmWwxmCsZkgwpoRyCUuktppNa4QEkszFBBkTDElyBCZnooJ9dGxG6HxkP2ghP6mWjKIy8NmzLdpt0QGYwA0jY3CCCmhIcaTZtFSNpa5a0DXoBlU1OFfR1JpN6zgeGmrTSyQmkjfU1tA1FavCoA0h8u3DwNuHPb/8xSOWQGMiLmdi8pLXC6Jscb5FD5iMJ4lqjBKnzEcRJiCKiHkiy3crRVXpIneqQCViDvg44ZMj5gBKMfqemALreoWpoO0sKIg5Y6KCUFh7jBz7d9w/fIfVQkFPzKuTknZMyeODwSVPyFFa7Rhd8rSawSWcP78uhbUV1laSC9QSCZridJ6vBfrc2J3Bn+eLtSpamkpJNGnkRkl9a46FTauwyQhiVRiLsaiVgCEriZxjkj6nts7UbSsQ3HXAh4gLkWGapPDaOcZxFPWIj2znRuT7DMr5onAyQpefuWB2/w1R4KduHx4nF4P7/fvkErBoPTuhhdWh1GkeaomM55rFVPKzzIZ/MQMn2ZC598RsGjOn9fEDY1zyffNiN8tBnorl81InGGNRrQnF5qQszze+z6H//u2T4dCXz57w488/o5/GhY2ZFItqjAgjl4s584akJmQeQGc4N0UlQM81JCWSOy9onx1RzvIC5fMzBh/HI8cw0TYNqWjnNY2RdkNty6OWpqJP7+64u7uTqO/8hocj2SdyiAQnBsiFuODMqeQexKi/FxaenRtlkp48JnntRPlVZ00jy78LXDqH9QlT4IJY4LDTnMmFXctyDrOcbvSBlOPFTKptLayqlEQUeZww1JAVLio8NbXWdFXH3VURX1SGY3+kj5G2lmci99yQszBA85TQRjrIGi3anZVWBDIjluM4kmKkWRt8EmHgZtWickQHhaInJdFrtApanViZebwYUhT9SKLncBzYD+4MTs4MIdJGiUzruqPWlsoAKhMC7B4n/FQRpwnb1KAttlpT6wqbDLoB1zhM06PCAcjUlWbTbajbVkQHtCZp6UyRtEySts7YjaL74Q3KlHo5kK4T2jLzXEJSHOOefnSoNAiTzYn+aoiZxsI46UslHAWmBVVl4hRLUC7kgBATIWXULHGiIOrZiZQ5RyFk9eOeEBOjK8pHVpN1IijPqAZyJYQmSgQdVCpsVCE/KTvh456YlNTAFkKV0mC1ImaPymo5p5gyY4woHTG2iFaZi8u6yFHNOb0MS5/sWWj53AiefigiE2UhLjlzEXeWZTUET0rCQtZGdDaVlWOlsnhWWDIs3eJTBh3nNj0Jk0WwOUeLtgmTEtpWi6Pdth3D6P4VY7Wy7qnT7xfv/ldj9z7pHH7X+1VVUTc1lTFLzbBRmjz3E1WZmaxy6PtSapPOAoW86EkbvYQJS0Spl7/f++aznDCzMVuiu7xEm6m0BMtZ5kZKc/F8ISgWI5jyx52Wj22fbATv37xms+oY3XQWBsNs3U8EEg1nnkE6C02VOoW3J9LJbJQKdr8YQc6MIKUO5twIBsZp4u2rbzkejtKotnR4r5uauqrp2o7H3aN4nyqx3WwwSi2Tg5wZh6PUfYXEWIyg96FIFAlkOXexPzdApyiRk/E/E6pWZxDOOfttvqzZEs7eLkAMTmoBU5LVlzkPKJ/N6aQWv8BV5eGTzrTyMhyOgxQljwP3jzum0RNcRWVLsbWKNE1L8Ak7Q0QoDscDwzAyeM+sC+SK2O1xHIXUUFh68z2yOuJD4Hgc2R96ERaPFTFbAoZ6LbV32o+MwyhMPFeRKvAWQiURR86aED1aZaZBo/Yjh9FfjIfdzkNKTEYz1TWNSTiV8SlxGBNvj56mVqzbiqqNaGPBWJpcY7HoBM5LQW0ejxiT6A6WzbGiagPe+dI306CrgeASh73DT07Ye9mhTEbpwjhGUwmZnpylE8Bv3/S8ftvzZjeQQoAU8DkQsqLPmuOQGKZ88bz8GMHDNM4YgCKqhI9yj9U8qZVCO7W0oFlau+SE1wODCsTkZTHSCZRHqUBwsbTDShDl2bkYoFICGcbEcXekyveAZvd4ZDrGpbt4NArchFaRcXDEFIhJWMoLmTlq/HiCslJOvHr1hmlyp9zaDM8VyNPYmdlIQVNY5pGCkgss60z5zFxTlnPGeyfOb84Ct5Z9QghLSmKZX2VtybkYxDKX5r1yQZJizvgQJG+fIsEHdrvD6bpS4i/+4i949+7dwlX413X72c9+foa8ZabJQ1ZEm4p6X6nzW9bxLJGgUvT9VPpJphkwWGKDuccmzK+pi/cU5+tpPlMLokSUJ1hz/n22J7GgYTGdEMdYApdcHEIXPj0SVL8LO1VKLW+a9yOo/5psc1T6N57Z7F0iEOTZy8un/zY48n/dt6X2iveglu//pWx5CcI/7TNnIOx737Pg+uq06+8+LnzsYaYzJZyTk3H5YWEsn/46/XMGV519xzlE/X3e+fl2HpGf7/oRn/5s4n58PM2HyZcf+zvdPnb4fPae3PbLMfNhmc/l5753O9vBaP3JYc4n7fWR4frR93/HSf7tZ/l5ake2uen1v+6baBafHOhPmQtnn/67OKVP3/JHf/34ru+L9Z5tn2wE/377++3vt7/f/n77++1fx+13GcFPhkPrpiv1KL8r7vo+t+x7PvGe5/6+wyge6im/yEe80w+/+nfb7UxmOqshqZtuSdB/ujf+KTv+K/gP70cJn/SZjJtOvdxsNUNL58/r3E0+j9U+drhPO4N5txkOWSjUCyx+zhxTXISFcwR+lndd0IYZOs9SpD5vq/V6IUd9LAo7HfsTzv0jr/3NT/bDKPj7R/3Hv0ch0e3xeDi9VvrVfazLyfc+q++9gL+LsPL7z0FphTVCdILM0J/GYdutljzgR7f/EtPkw498/735V/biC4Mxp4R3J7FpPUe4nzJPCgKllP4AwfgQSfnI8c4XxveglO8DUWbGwLxmfrxrj5x/WEgkivVmXaQe/263CyTm7P8f3e8j0/p3jfCM1DX2x+Pv2Ou0fZIRVFrzP/lf/ns8ffmlFKMWZpc19pS4zIGcIzEJK0uhqapGbqh6jx47K7NoRVsZamOorcIYqfGojEFrKRQObiIER9/vmaax1LG5M2MoBZdzZwqVEzrHMugyubw/L9CHw57/w//+P2AcR7Q2/I//F/87nrz4EXlugPSB4TgfEOqTFhn5dD6biL8DFjt7d4HJ8vmgPTP+6fT3paXMHHZv+b/8H/83eDegteK/9z/6kvWVwcVRlGi0xahOdP1iFtJETMTJUWuF0plUS4+ylCOH3WFxPFKsyElq2Gwl6jkxe6YpMU0R2xjqGro13N09Y7264ub6M6apZ5x6dvu3kA3GrIo2pSalqZAXMl9/9Yah94yDZ7VeSZ7IQFMrpsHz//y//pwYEnXT8B/8h/8RP/jih4UtWXIM5/mE5T6e7u5MWCPPupMinxcKy1JKdErH7izP7kQAP89mzHVVqiyMUeTvVEaV7uRRQZWy9EtUSVrnIPNBfgsYVfPbr77h3/mf/U+ZpgmlNZ/9G/82Thne7o6oudhea6kX1fqUF01nzoMMlnnQiOOhFXMRV0aJ+LvSIkdTDJmCUhpx+vx8fiB1miXTL58vBe4hiKrJ3FuTlFAp8eLJHf/WP/5H/Lf/yR+jpp5/73/9v5LC7Lbj3/3f/vtc3T6R/Hqpnkj5bNy/XxANZ0SI8tDK+pEoDZnne5ARFZFMYQyW531GaEs5laLqeU6d1e0u/54c7dNrhZFYWsI8vPmG/9v/6d+XfLfW/PQf/CHGmKX+Lwsl8TRvtVp0QFfNiq7teHJ9S9t2S63gnKOcewxO44TzThRU4CSLVlUy3jJFilCELE56mXMBeWJtK6yWetDeTUzesdvvy7ocCW4UTVKluN5sGcaJ//if/lNyzlxdXfEf/kf/Z+6ePEErLd3XkDmyrEYLOHhSi1Y5vzdL5B1tK5lJufRNRSZiyoFEJESHUqWMJkuVwMw6lXkj33/enEEh7PYPt8tR9Bd/9i/4d/+d//kFhP192ydHglWzolltCSEWtpeUN1CSl0pJ8XVKvlCTFbWtTwSRhRxyYn8ZoLYGq4VJZE0uPyJ3ZlRCZflsbDRaV1QV+EYvdkqOIpyknJIIR+ewrAX5zIiJR3oebSjW21u2N0+YD3gRPSm4kHx7L6L6WKrjfWfuY96d4mwCnvlE8+J9sYifT9xzuugSGZejvJeg77qKrjNkF6jbBmsrqqpbRpWtLMF5pj0k78QJMBlTicFUusgl5UhwWvocTpqb7ZZV27JqLMdhYnccGLLD1JnVBq5uW9arlvWmIR8cPirqpqGuG9ZrWQQUMLn9ImzcrMFUlm5ds1qvRLbOaqxRDAd3ygOieHL7hOfPns+zSibdUq6y3N1L5/HsGYjogEywWDhHOs/3X2qcZsmzk/U8VYxb8aZAKVlgS92lSmIEk8rYLPT7oOeno5Cq80TGY3TNOEzL88ooPFpKVKxlUQPSRdDdmGKoT0Pp3E1bBsJMGS3GUCtNLl3JlTblfilOpKrM3FXlNAoTKpe6tVyKGpUiaQtWi1zfPD6zNHNt11fc3j3j+fOXpGG/TAylFFe3d9zcPStEFApzbzbgpzzbuUO4aAEvhLrCKs0zSW1maothFUNCIb2cs7pPzOsZabio3/0eIyjD5QydQOHG/sL3VWX/GGMp+QBlSkuznFFWyjHQirZtaduWppF/q6qiqqpiuEVFxQcRhdBnhnV27uqzki/n/HIfZsUZpTUxiF7nPB+stXRa5jmA8xPBT1KCA1TW0jbNRSmBUoqnT57y7Nlz6e6uOCvpmg3i+VwqrlYughuAyhC8w/uALl3p66phGbwKhmlgdAMPh4G2btlutrS2K0xgvdTFSoMCzvSeFTqfjOApaOG0bpYx9Ob2Gz51+2QjOAwjx+NQHs55XYdMKGPnQRmkwWzOJO2XATwbTkmUlxGYMiN6kV6zKlKZgNURRURlT06OnDw+uMUuzRNHa0WmQmpfKhkcKQKh+LYzhFO8xqwuSBZQ1otFNeHs3w8rOC+W14sHcL5PPvdcTp95P7t60ltVywIsbxSIpXh+4jmdBhDzRFbzBKXci/errxU5KvwAbWOpbE3bVaK4rg1V3TL2PXHo2R1HIQtpS7de0XQ1XVeRmYhpIOcoNicabrsnvLh9wk+eP+Xd/pHv3r3lV4c3JBOk63TTYCtDiImhd+weJ1K2dO2am+snxQMOON/j/JFp6smMNF1L22zpVi2maA6SNTmOZwYuo5P8xCXSQ+TL5vuwPJtLejZK6N2ZXNSOylGLykScF+X5OLlgCBlikf3TuYj7Zl3k/c+MR2EZ6pxQJLLOpYRILwZymaRKnIuzh8V+GMi2RqkEsSx0adaKzTAXVItndwZInK51+Q4lxktZMZ4yvaIUMaMglfKApYY3L/vMba1AoxLiCAEphKVEASWSayjptdi1K667DV3T4aO/mBZzcfzSeybLM5p1PWdRgAuzXubAuWyYpCzmYvSTgdIaKS+izLsknT6UFjh+buR9blhFc/LcCJa5pM4MpTIXkaF6DyKcpolc/l2tVqK1qs+M4MwMN7rI0Jnl2POP2EhN9GmRTpO1SMo7ZoariFhXRXEnEEJeFGnmqDADIQSmccBozaptqOqayho2XUdPog+uIAyicpPJItR/tp33YJ3XrDmCkxt0GrMy+gT1MPJ00DkzHg8c9nsUilXXsb65FdWpMjSnwzvGh3u+/s0vub17wvbLn9BsWmxZh0OZhyqXum01jw6FzrpEnmVun8nDnTsx5r11/ndtn2wEuzqz6YrE0VyqkDMypIvxS5GIR+mAImH0kUUJXqtlAbeoouO5+BIELyoVuYivKiIqBbSS6M7iFx3C4EtdSAygQZGEtpsiSUUoXZcjmTxLY6DJWRHCeDI4y8M8c7EvtvPX1cXnTn0Q37OExe6evKcljDkzhJffP8cD75nncqjTBJ+N4nlk8+HxZHNxwiaBI1IIEA3rzqJ0JuPZDwfc6AjKoytNbWqsXdGuWuq2IusRHyF66RT95OoJ//hP/m3+9Msf8/ntLW2YOB73PO4e+f999zPe+gNv8h4/JbIfqOwBhWW7uSGlSF01OOc4Ho84N/Lu4TXjeMCHibpqRMy3tYQw4vwskKs5Hs6hb4HQtYZYWnIpFKboBsp+elHhO+lUlNdIVJQyOb34GsXfKQsJszyTZkY5culdl4pbqhEoNikBV5XKoHRRTpFyCUgknVFJxn8odVYac+kllW1p2ZWUnGGOxbZqCVmrClX0ZHOcdWM5RX+zU3Zy14vjJ/WjOUZQpqialJqvWXxxcfiKMc9zXZcSOZw5QpuJnsZgrOQAX2yvefHkGTerLU3VknR/cV3ftxQV7Ia4mMcLM3jhgM7GMM2UeE5R20XpUj592wUScPG9FAczXxjC094fOWN1fmKy9rx5+xZKJBhCYL1a8dnzF4BoAr9+907aV+VEdBHXdhhO3R/OhTQUJxk2pdQiuzYv7OcCIedybM45QgilG4M4NSIenslEOhKVkW4YldE0VYWdn2dGBL6H/uIytZqDB5bC9AylPvNkjJRSWCX7VypjijG0KpMrTdKRn/35P8PkyLtNw7quxcilzLvHPff7A6+++Q7z5U9It7fYzS2V1sSUqJXIclZna+5plTw9K5nZZe6W5gKCxkCjL0UBftf2t+giMVGp6bKoNRWFlxQhOhQZq0Apj4hxueK1igWf9QFlschnhY6ZnDxzrkar4iWRsWf9s5q2pmkaxlHUHHzxXGVZC2QlEWTWcw/m2dul1N0ZRGvrvW0R3S5D8iMR3gfr1lztezl1zz/x0VcETZgXsPK7KvFgPvv+fHr/1IKxnKcqea65l+NHjGLMgYzGWFF8N0ruvYjMBqKXonY0mEoKqI0RixBTAhVLXiRxtb7lxfYF//DLn/Llk6c8aVvi/h6ja0zdcVd1uOS59wPrqqWtGppqS4+nz46htIZyU+Bw3DNNA30/EEMgZahWDcYI5XycXKn5kec0Tf5ioVJqrjNKJ+FgUZkT43W2YC3Tp4yn2bGYIW+tTmZSKRmLptx/lUFHTVISLeUSkSXM8nAkEs9nx1y+4b3fTt60zlpqP99DGdJ5FLRcwinTopQRYQmMwIk5lcJl+YwMgbOxW/YpO6NK1GT0qUfhnPkkq+Lkn59xiQbNnHMvSv/lrVkz83qz5WqzoWvqIj/4nsUoF3MCOJZfAJYoZrHfUCL18vvJwi3/n73983HxMWO7RMGzaP/Z93Lx+UuDeH4u5+Pm/Mtmcl3OiWmasKXLhFYadMY5h4+RSCLUYalLPLnceZnOpnSUnevqopp9m9K15kxg+5zgMpd6LZBylvyiIknnlxRhrsdUJxQupUQsXdinaVouPMMC0ZLUyRAzIyyFGFmQh1nI3iiFKVGgUSwqSjaNpOnA4II0FFeK6ALHfc9wHImHPbHf44cDpOLYzVH0cs/PnheSbpihT7ER53vI+IycJ8H+5u2TZdN0fIMOVfnmov0YNKEfmfqRTKBpam5ursk4UvaM4UAIMgCqpmXVrbi5viEMUpS8H3ekFErhrSMpRdIKYxu0rWjqjq7pqGyF1orNdsN6vebh/oFhGNjtdiRSSbQ6lIpFcHg2jJKrJEMOGmMaqjpfDOh5kC9G7TzAm3M26nJifLC9bwPzuR07CwvPDqJmw1sW5NNEnM/j9DmxheV9dQZTzHaT/MEpJO1QlXSG37Yb2qohT+Cjw4Ue5aVzQ21q6rUh5sTkHMfBEY8Z23nIHpUz/41/+G/wB5/9Af+dP/pvsv/qNxy/+YaHV9/h/ITzE+1uovMRfOZPf/LHfPHsM67aNf/yNz/nr77+Ja+Oe5SyuDHx229/yzT12DqzWq1Ztx3b9TUhRIbe8frbPd4HkVSrHNPgL+68mpvfRtC6XLeRyHBexGWRF49dKREFtlojU7VER6Sla7qgf6lMhnKcjMiyZaQfgcryuipqPsCZwF7psKIunqcuxbsxK7IWUo5BS+G5uZymaYa2VVFGQcnepkbbBl23YgiVIZZWSTH6kwFchphaLISgIPJapQx1VVNVlSysITI5J+o98yKoFEuzUF2VBodW7k86RSYKaJqG26trPnv6jOd3d2xXQvh4HzY831SB+JU6TQRjzpRCkAggcRLIn39SSgtRYpkV54P+MqRjFqfQs/oWeYn2zz60nM/5eeXz+VQM5/s10iGGZZk+Ho6kGHl4fJTmu8ButwetsY000q7rmqaVDg7aSBuxmSuxalsyUq/nvUeFRGXLWI1wOBwWeLXrVujSNujYHwscu8aUCDMVFKJwvkS1JYuwgzWa5APBOY77A8Ox53gcLu7JFCfGICzYEASN0Ephki0qPaLAZayi1vVS+E4U2DwmTdut6GrLP/i9H3B4+1sevvlr8ujx3nF43NFPkeAz22qNCT2P717x5PMfo6uKWU5STO5l9CemL0mP2HkMlH8XlzMLsevvRDaN9EiONeKDQ86KPICNitoonj1/wnrdcX2zlZxP9Lx7NByPPeM0sq1r7q6v+eEXP8QdPMfDkV8cDozHI4PrqVcVttLYyrBuFU1dsenWrJottW0w1tK2DU3bYNH4jeN6u8WHCR89vTswBYeLjjFMpMJU1cX1ykrYkB/CoWeG8PTHKaJQ6uxWv7cp9eFrpzfPfp9zLlzO3QvLeoJoONt3+aMYyA+ho3KB+jJcWnc3XG3X1KbFqEzEcxgGrNbUao1WDpcTkw/YusJaULYufXw1T6433K42vNjc8qdf/AlP17e44z0hjITseTwe6Y89fd/zenwg1oY/uPmcn6ye85m9RY2OH6iO3N3y0PQcfaDvJb9njZVIrgxdrRNKB1AjdSsweQphUe+5XPrkHlh9ui/M+RcluTwRYzdkAyl7pvGBXb8nuAnbdtRNR9OtpL8kmhQpYr6nyEQEy2XZtWg0BXXIMwuxsD/JRJWpUi6TdtYuLXAqsxMlTysh5Bv7vkc1sy0zKGUxFlZVh7Y12lYEZEGrrMEriQaDycUIKtyinTgv47KUV7aisRXPb25Zr6WfXc4wOcfhIN0TfIiMfmIKDh9CISRYkqlRxhaCR0RnIVXUxrBtOm5XG+7Wa65XK7quwdrfLajxfuS1iM+fjdvZ/MRU3IxzgsuS81EnJmjmffu3CFkvUdP5/D6DQRdBidkQlnE2R2CK4hjwoRGUHol6MaxKaQ79QM69fK82tF3Hertlu93QtS1ZZ0Y/4UrqqLaWuqqoK4HuzqMacVQ8k3eMw0hlLV3TUhnRXvVlbMYY2B8PS95QIzJnFCUzRabWhqqp6ZqKyRmc1VQ5YVIkhpNwSCYzecfknVzXnC9UInQvWyTFhEsJncMCnwprOAvnQGV08hgibQU3mwabKpKvySmx93t613PfB3b5a974TF7d8uTZSz57+UMZuTkjWq9z9Cy6ueembe4IorIqCsfi9Lng8fFMQetv2D7ZCOZ0hNTJwMmgssJmQ2NruqrjB8+uWG9WbNYrfEq44CEGbMrYlNg0DdfdiqdX14zKY5OiUQYVA9lNtNuKutbUnWHdVbR1zXbVsWpW1LbBmgpbS06wNoYUWzarFc4PuDBh+8jgFJOHOIwihq3SWdJAE1MmxPHiui4G94LkqA9/53K38uEPjeAl4jK/xOxjnt6fZ+LFES8/V/ZdIsrzg10c98PDNM2art1Q24YYe2LyODehVUdtGrICneUZGWMFEjWKnDQGy7P1Uz6/ec5Pn/6QH12/pNWGaf+I89K0tR8Hdocj+92BfR5o7YYfbJ/yzG64pmEaeq6jYTIda1MzuchuGgTaNrMe5HzCiZw9MU/YSp6XT6INGOOJnLD4KctSOUdnJ/jIqlzmgsIriZaG4zt2714xHPdUmy2b7Q1X+im6MWjsQqEXvbJTp4JZklxnVfIh0j3htIgWso0ClSRCT7Aw6hZPtkTvqhzTvuezyJfFRTRYKY0xls1qI+LTxjB4jzWGpq7wOhOzJiBdPqRyJoqYsEoLaUgB1ljauuXp7RNurq/ZbjagFOM48djt6YcR5z2P/RE9Dig3kUKUHIupycaWQSbEIKsyq7pi03RcNSu2bcu6bWga+0GpyvLIMpzDeKr8f4Zy59fy+cBmRjokdzmXV5wwnjnCO/3AaT5fGMAZ7l0MoDp7hvNXZt6fzRd+6HtTVJpwm6UnntKa0TmCl355KClh6LoVdS0NtGOKJD+BV1KyUDdorZbyl5ilUCtJ1plYyDHTOKKaGtU0BdYUNELWhsw0DtiqIuVEV9kzODGjM1g7s/kVVklPTZszKTicdydfPEtXdh/FuAmMPjsFJcJfKgACLoWFtZqKYHk0FVFnTCE1WpVY1RaTIRrD6FqyPjCFwK6fiD7DELh69isSiucvXoKqlns+P2ORcxeHM5fBoc+W6TTn0HPCxbA0Bf+U7dMjQe1AOwyWJ9trrroNX9y+5HpzzfXmCm00IXiO/QGtNHVVs/niS9xzj3eOcZB+ed/86ld4n/DecXXVsF5/RlbPePb8ltW6ZbPpimU3WBpSEGFtpRM6e1SMklyuK6ztSHlNSJHjeCU9pnLi9bu34tm6kVdvv2HyI8lCP05SyPsx6ER+Ob3G7OF9bGqUJwR/I/Y8H6P8cvb5eSKfLQ5KXUzeZaJybhAvlotlsX3f8Bpz6hVmGytNaKuMygaXMlNyAKyrjqfXdzStRPlrs6VTHfWU2ewMbrrnzesBkzPDcCCbhI+eh8OOd/s9b3d7mpcbntw94w+e/AC/P/DtwwO7h3sO/YFdf8Dt94zjkX2/o6o01paGsBZQkf1+ZJr2HMd3KOWwtWG72rLf17h+zg7IbPBJ46NBxzNoxJiCTmRQ0hw1uJ63335Fv3/H/W9/zjdffcu7d488Os/ds1te/vAl/+a/+d9lu7lD04qfmVMR95aITuVQnD1NSuKVevIyM02mNItFejfORi+fiUSXp2TKM4xZSFzhPTSCGCGUV23NarXlH//RH9PUFqsVb+/fYbSmraR/XsyJ3jv60TFOnvv9Eec9LgT6FIll8Wpsxbpb8/nzL3h2e8Pd1RXWNoQYGcaJ4zAwTo43xz1vdg88HA68ftgRMkRlSFrEpZOf6CrNqrL88OkTnm42PNtc8eLmhrsrYfVas2ThLsc4c1R2WrV08dou6juL0YspXxBAlvkx/y9LoE1W5HQytHPPv1wcCcjM/fNOsOd8gPnsymmV10/QKIV4k3m/y4FSis16Rd0IoStngbNdKOTAuXtF8Ez9kUeSlKvkxKqTTjZGKxxSS+enjIuZw+BQVQuqhXZN9A/kaYcPw9IhIwSBsCfnMBra2uL6I5DEKSOiK8uq7ag1VDqJGH5OQKRua1Rbw/WGJzcbXr2955/9y58va+LsdMRcxmSJwDKeoEuZT4qkGBmRfKC15fg5gx8gTGTXk99+DeMDanwkqYaQNEdVs8uWh6i5H3pc7wi7I/E/+//y5u1rnr54wfrqKVWzJoQ5pQXC+JeuMafxkJd2azF4gg94NxFSZhwvCVq/a/t0I4imshW36xtePnnB3faGF1dPaeuGpmro+yPTOHLYHzC2xtiadbumtpbUNNhSQ5NSxpgkUV9zi9YJYxW3d1eyQBrNeBwhZ0yh1Yr9SGhtpZBal2ggJypjsFoTbSNFpymxXW2oXc2gK97wGpUNTVOxf9gzju5imqoCgc2T4SzckAa7vB/ZqYvfP4jhPhbUzSviYghLVPje5+e8Bfn8g+cTmJJLPNU7qvPPnW0+9LgAydTYwuyyVuGdJwaPSrCu1jy9esbVZitduRPkMRNcYtjtcdYQmhqaDSbDNPUkHQkxsDsc2I8DffRc2YZG1+hY8hTjyP39A/3Uc5gGfAgLs61rOyprME0iq4CLjjB66X/oC7ykIakg5XLvUZ1n5f8ZFlvuk1Qmk7SW1iox8+qbe96++obf/uKvOfZHxmnkcUxEJdHZ/U9foY3hetuWDgOpCFXrJe8wF82fIpDziEFQkZxOEOrspQoZNBVY/QwO/yB2YfmeZQAVLN5WNV989oJnd9c412O0sHgpLLrBiWD5oe/561//lv3hyO54RB0TU4wMhbk4eseuPwpdvQu0dUdtDF1lWdWWyXt0LZ0ZGltD1riYCSimHAkx4lKgrSpWTc31esP1es31umPdNrR1hZmjwA9QkPnu5eLYqYX4phZYZNZbLVF/PrtHJZI7OZBZDGCxjZpTd5bTjDm/r5p5pBT+1OkvdYbQnPys5UCKuTGA+sDZrStLU1e0TQ1INKedwIIaMcJNZcV4ZYoQt6exuvAeWoIy9MkwjRCyxqkWrVcoY6WpcmuADpMqslVM1mKUReVMSJGspR1a08VSZ11SAkoXok7JpUZBRGIIJSLUGGtLOcclUXCus1RKHAhKt/acPKE4MLmQrYwSQYAQEzkFiRw1qOhQccIUYmLwkaMbGTy83XvePg7sB19AukzOnv3uHQ8PW96+ewPVmpVt8ZFlnBgt5xpSPImfp1CWU4lqYwwE58lKE8LfQWd5haa2Lc/vXvCjH/yYZzdPuGpWC9NofDdyOBx4uH+gW21ou0xlCyW4tD4JZTHMSggL1l5R15a6Nmy2K2LwuGnk+HAgx0xdW3SakXJNZRTWSieFnBM5Sg0NRmDZyXtSymzbLY31WF2hk0VlS9usiWHP0LuL6Gsuqv8o7KE+fOn8jwuIcv7nvQOpsx1mr/hij4vjlUhwnpALXHOWL5ROqJyz3TKzZz1vmckfGJ3HVhWt2kgdU5WZsmNyjjauuVld8w8/+32srcloBpe5f3jH7uGBh7fvaCwcWkNY3VIpTZgmIgEfHfcPO3bB0+dAZ1paVRMnUdvfHw+8vX+g9wNDnPBWWHI5ZSqzoqlrbDsx+Hsm13Pc2dKurQIi2WS8cqRgEZf0dF25TMry9ICMSsWTz6oo2yhCyHzz1T1f/fIb/urP/opmW2Fbw2HUuBgZh57Xb35L3dTc3LwAEhSlFHlOs7YMJBWFtPXes4I5elEFAi3PImVUyguEk/QJGp1HwIeL9eUYEDKm5QdffMGf/uFP2XSF5autkGNSYhx7HnaPPDzuaLTh1eu3fPtakYLnMDqGyeGcQ+mRVw8P1HVN1zRcr7Y0dUVXW2JX4WOgbipaa9k0LQaLixGXEns/MnrHIThWdcOmbbndbrldr7hZr05GsDBDL506iSMgSymTmp3OSwQlUdrfcCp4XwyhmhETteSHFMJUnWt1BUKcfYyTYT25SWl5oql8ZpmR6sSslCmVl5F1fjUXvytFXVvaRoyg0qJ8pJWClHFFYaeqK5rKkhTEmPDOkVoL2aJsjaPGR8v9BNlYdNVgzQplKrQyGLVBNYGqvobs6XEEJbm4nDyNbbAVrLKGHAVITX4xcjmJE+SDx00TbhpBgbGWpq6L43kJG6bFGTmJZeQo/SPlWQoyo1BUxdAGP5HChDQu11giJjmMkuk7TpG3O8dj7/nqzYH7w8BhdFLWVrpJ94cHHu5XvHr1Lc32Kabb4kqUb1SmUnI+zk3SOSRGYpBm1eREitJTMsWAMTUxXKa9ftf2abJpwLa54sXtS/7k9/+UJ9d3rOqONE1EH3GT5+uvv+Hdu7d89913/PDLH2Orhqqw0bTWVN7LJPGq5A8gFexZmPiKFMBNCe8TKSRUmkih0LxNRZs6GqUWr1OiNFkc1t2KqpJu6mMpCs05kxz4IRNXhuPOs7sfuHB+zp1X9ZHX3r8R5//C4v3AaaH7wPCpBW1gFrTKZ58/DzVntGY2qIXncSIAzMcqv8/O9Ad8hOzJRBKew/AI2VIpYZa1bcO1fcK6uUZhccMRHyL3Q8UUDaq54uaJwuiMtYqjNuTomcLI8XHHcOx59e4eu1rRXm3ZdFcoZfnm3RvevH7D4XjgYb9jMh5nA5sfNNQo1lNi1TSgFIfhAOqI0gOZFSSLihZlWpSK+BCpKmjb9+63TqVH3iyYJxG7JpXyg4CtFJu65R//k3/Ey6dPMMM7duOewY+srafRmRWJ3TffsLYdL7/4A1KUaDJrRVKJRDodf4ZHETUZkxU6sxjGoDKmkGuSLpCNzjI5lZT9pDTnV0oR/Hs1N8szJZG8o+8P/OI3v+Gf/PE/oOtW/PDFFQAhZHwQiK99siW+fIqPkd/78Y/4+V//ij//i7/iX/zFX/Bw7LmbHO/2Pc73vHv7DY2K2By5W69Ztzc8vb2iriRKWT/u2TQ1u9WKbbvCp4hPkfvhwOAmdl1DZyu6qmJbV2y7huttx5O7G1ZdS9u0S5Pb8+litMYYhYosIhvnzmMsLN4QE86NpCBIxeRHkWg0VoyCrlFW2Okz1CnPXv4VhERivdOUkpztPE8k35qXgn252+cOzcwgPUltlIOXcpwyv5Xi9uaapqnR2ghMFyJuEse9qiyb9QZtVKkR1aQq0VSGpmtRdctebTnEjkNaobfPSIB3E3En6ZpKz8pFQiTOSRF9gR9Vzap5iVdgFVh7A8mjoqc1DmsyyRj64UD0jmPvCd4Rgl9acGk9EkJgdzgu9xIyKkekLVvCTwPkiDFZ8oA5kGNGKYPWBpeKBGFKhDCRU8AEBTpjVCRGxeTg4Rh5vRt5PDoe9gOTC+JzoqT2NitSDgyHPb/51c9ZXz+RXqC2EbUwBTlmcvS4sWcaj8TgpawuB1LyeDdKYKU1Xlum/oFP3T5dQNvWrKqOq25LrWtprumF0jt3X+77kcOhBwoD8D0cXdQjhGSgZ9RnxpmSIkVIMS8QRiyNbkVjz0vNS1WR9KyXl3BToYlnVbrCZ3yWEtxKWxrbMCmPnyK+GNhzP1wM6Vx3cgr9zg3Zxc7vWUgplr60j+qDT56igPn9uSIjv7eLKlDnKUycYdTlEOX30zcs/JqzLWdDzloGbpbeaC5YqqbBGrCmATTj6AjjAR8C09SAEg/WmJX4zSoTYiDmjMswpcwUE1NKJR9QkTM4HxgmzxACLkPQhlgFUptptopKVzRhJUn9lPCAqiuqkEiuIYyWMEnvOKVlIVtkMC/u5AzVzDdxAckWRRiZ5Ir1qmHctlxfrYjKk1XG5IzOkRQix92Bfn8gFx1SEVSYv2iGndWJIZgvqwAvfi9WbPmXUve+wN/yLHOWQy9aoOcPMZcoMWViCOz7nmGa8D5SGWk4PHlH3/cYo7la34KqqHPm2ifubq55dnfLj3/4Bcdp4hgiX796w/44kLGYHHHTiJtGYvBopQTOU5mQkpCitCVnzRQco3eEVFMpMOtIYwyNsQVGrVh3LauuFca2sagPRiFLI92ZuHROCpuREZE5i7ixJwVHDiM5+pJ7smhtScZDalHaSE2p5ESW+3qxkMOpZFJJFCn7niDuE1KjLv3Q5a3zSf4hO7SphbwnPQ4VqUqUWnBApNJKzEqFJaWMDw5la4Kq6WkYzRpvN9TNWmDDkPBxJAdH1Kd+ftvNBq0qVGoIkyBZYzbCGM6ZWlWooswjzZ0TOiSmmEkh4cNUcqwRZWYhBMm5TiFdLFY5OnJy4sCFiZyDqO7kEmmFIGgE1RK5J07v56KjmpE+jJML9FPkMAaOU2BwYelBmLMpBR0iEBCc4/B4z3B8wA231OurUo4nYz8FT3Qj0Q2E4CD7xTgHNwh0bQwoQ/QDFxf2O7ZPV4yxDeuqZW0a8IlpGjkcjng3MU0DzgWck2alXdex3W4BFoqzUkJ9nyPAOVk+N9VNAWKA4KGyHRqPHyfGccK5iX50+BhQRgtF2HuOxyP73R7nPMEnmralbhuarmO1XnN1fcPd9pacFK8f97jRE8PljVkMcuaDgf7+nP7gbXVmAIthOuc8zuuc0HvVxWsLK27OwL/3RbPc0+yZLhHhgpOeIsPZEF+ceFpBtGAajO7JBAY3opSiUhZTW5yLfNe/gfENKQY8azZXT1ittigsKQdC9DzuJnyIJFORqgaaiG4bqqahrRqOg+OYJobjEVU1sNpg2hqzeiCtAqsniqpuqNSGvj/gg6fddGTVkID9dcdul7h/F4n+CCmgsynR7CVcY3JC51Tu51yjpwp9+syziIkUj2jdc31nyFVHPWgisN8NPN4fef3dG7r1Ldl7jJEFzUexUiZDRuqWkjrpiZolPBdijFqMpFS36aLNCBDRxHlE6IJMlMcXLoSjS53gWU4spcRhHLjf73j7cE98ecs0Drx5+4avv/mGtmt5cntF1pqQEvv9Aa3g+dM7fvSjzzHWoKqKv/rZz3n9+i2//eY1g8tMbmQ47Oi7Bucm7PWapqlpu4627dkcB4wx7Pse9p6t0XR1xbW9ojaGyhhuuobb7Zq7m2u22w3W2sXAvJ9jmnNQ7xueSF7USHKBs/r9O6I7YuJIVZVcfxSCd8gazBZdNZhug6pr0ObCOVwy5MWoiV8jDrLMFSF6CJPXMLsyS663zK+5XOZ8Pl6Q5hS0dS3SZNayalsRIcgJU8mC0k8eHwqJy7SEmOj7I4ekGah4m9eo1RPs1TOqupZFPUXCuCMlR/ZHkpKSmCe3L9iutmxXG7779jX7Q893D3tUjigyrQKrFVbX+Bgx3mNioA5AyOzfPaCtxViLCiVqRkpJpnwyFYKc7clOUk5xOoiR0RGtg6AYzoGt0NSEBFJxSymfyejgIXpC9IT9nv2h5+1h4u1hYtc7DpMjxSB5Ri0kr6wkl+rGgYe337B79zVXVzVNA9Y2Am/6RPIjfjrgp53k/LKD0rgh+UnmYbTklAnT/lNt4N8iJ5gShEAcR5wPuBDZH/YC7WUYhgk3efJMUMgwlk7kWutlkGqlIU2kFPEuYG0NtiK4SAqZnDSVbVBognKEGBjHkVevX+NTBGNYdR3DMPDdd9/xzW+ls7wbHU3X0a5W/Oj3foItdYWfPX9GVdXshkngl+Av7o1WQsDJBao6hWTzhS9W66PR4Qcw5DnUOU/EfPrsPNHmQ2XOZ2yBd84poWfHmnedP3uKiD58XkZ1GBoIAXSDJrNp2/8/bX/6ZEmWZPeBv7vZ8jbfYsnIraqruhtNLARAgDMkR+bDyHznf82REc6QAAeAoDd0ZVVukbG4+1tsvdt80GvvuUdWg9kigEl6RvjzcPdnZteuqh49eg5fvfyCL28+p5lbHt7v+d03f6DSnqauePlqTU6Zru9LYhCJOTBOkxBqfMDWLa5ucOuWdd2ybdZoZwkxMVUV1WqLto6t9vRKeguHDxPaBqoqSI9KiSXSq+0rrtpr7Mtrfnj/wN/mH/nduw6fI9ZpwgQhPr/eMSpSLLRyVSrrdKkHUItWUOLYH+innnZ9xWMXGOeeb759y+wTIWrqY0f1/if+8q/+d9588Su2u2u0WaGyufSgVORcs2SRTstKFwhWlofNi+LLpbksqidF0SYLXAsiFaYV2E8WjkhjyqacNWANUWX6aWJ/OjGOM1obrq+vMZVsaGjNoes4dj1/+PZbrLHc3Nyw261RShRFfvP6JS/ahq3W7MfIccpUKhHGjvsP72hrS96u2Wxa1o2DFHgwCUNAp0BjdJmfF7skZzRtZVk1Fet1i7YKZcqzXdxlnp2XnFaZ75J7eR5xKOs+5UjyIzoOqDRS65kcREB6t7sixowPiX33SPAW0oTLO3TVgGlYwE35meXTMmf49JkREQRVIFGBNxOyB+SlOl2qy3MgLKX7J8jLMI1UlWXdVLTW0lSOTdtgKsg68+HUMwTH6Bu2u8/wIRHuPzKdJk5Rk9e3VJsr6vWKMHTEEMTpo1RyKs/YaoWtVxyHGdSEsQ2mrnDRw2NPjLNUy0mSbw0MVlFbw3Z9Qx4SBs9qu2b0kd5HSHORnIzCaJ2eEEhyYji9pTcCyfp5Qnr0SeQrVZKh+ORIyRUEhDKGVuYEo8ekiAmeQ3fi8djx/rHn/hQ4TYnTJJ0CQUwmFsg6qwhzojvuuX//I01taSpL3a6p65YYE9PYczx+YJ46UvQYHdE6o1UGoqCJKUpFPc8/3xT/nuOXs0NzhhiJ88Q8zUzeS5AzUobHEIsYq2RdKcoYRM65CL8+ETqNgRgCfp5RWaMxMpuUyoyUFcV7rUVI1nvP8XikbldspglnLcMw8Pj4yIf376UanDztes16s+GLr76Uk9OaVdMwNCOkXFhS8VkcEzgrl4pKnTfWfPni+d89/fPZpfnZZ+oTTDQ/eRBLs/5ptrlUdudvz08+ufy4Z9/2RDotl17H80MLJJrAINXzdn3Dm92X/Orua/JhYr7vGYceryErS1VVTCHjz7NDRWKtEJpCzNhGRHld7Vg5qQSzkmpkzEmGZauaqrL4uUX7gfk0gkuEICw1Zw2t3fCifc3rzWds1DXVXNNte757r5hSImZFzKLY8vQCp6zOtiypXAsVn1xLrYTNOI90w4nZj7SrFZkHhtHz/sORrAy2bunGmcfDnm+//R1VU4PObDYGTYXKThiieQHtygaei8p9Kf91zsV+aXmbF/j0vOlyGYRecpaniMGzlVUQE7Qm5sQ4z5yGgXEOtI2jaVtM5YrlDBz7nvvHR47HE9e7KzbrtUBxWWSsrtcrKhL9ZoVxmaqGHDOkSN+dGMeBunE4vaJ2mlhZnAGrpRelnYFs5By0sLerylLXjrp2pc8n71+pP3JW6smf+ZIgPp30JEVinFFphjRDGpjnCVDcXG1wVuO04ngciMmQZkVylWQOqi6VmiQgixj/As+cn7diq6bOaM1CfVnINk+S0XOvIp/v1/PTyngvtlK6sF2tVqzrGl1BNonae6KqCNTUqx0qgZlkdGf2Ceo12jVY65hjKoxMU/w4kSpPgzKaYRrPdlYpiQ0RTOQ8y+chn2N1okLbBt1uYN6LBmdVMYaROQSilwJk0Sd9GiwyGT8emat8LhgUiWWwVSth5AvmkkCJmLWU67E4+Hh0jKjgmb1nnALdFBlDYo5PqDVKE4t7TaSofAWYhoHT4wP7tuH6akcKMxRJTT8PTOMJ7wdy8iSdsUYt/DiZ0khI1Rj+a8wJEolpZuwODOPE6AOzDyRj0VoapcYItDlNI313woXqbBuy2mzIMTFOI2E8EfzMPM2s2oWSa9HW0TZimhqdJ8fAXj8SQuRwOHJ9c0flKkiZvuv57vvvuH+8Z549q3rFZrPm5vaGL7/4nKurHXH2nPZ7Hj585O0PP3DYCxvxj4Ut+bN4CqLOskPL1/+I4ujfc/yRre3J7neOb0+D5BLQeFrdXTLZpXZc6ML5/KBfAuET9UnIMAwH3DBhnMKZlrXb8n//i/8bX6yueVWv2E/vyTfXHP/st3zz/U/MWYG1mDiTs6efw5mRarVGWYtCCB7zHHHG4nNmYEYpy37o+Ls/fM+f/raibkSst+8aOO6oGoVPR3o+ogg0dstvVr/mN/Y3vM6vMNHTNnfsXkf+9u/+klN34N3DiaayxPi8svARfMiolIhatjQPxUMyUxnN/f17/u5v/opx+p6mhj/7ky/4y//0O97+8IGH+4H11Ybd3Yp5Gvnp7U98/PjA7775hhev7vif/sd/xdXVl6w3nxPnWCqJ0oNWZTA9lHuixJnBZH2uQtKTFXX5fy4OFJm4ZDt/hJq+5FxKG9CKyc88HA+8/XjPTw8H7q53XN9sWbUy0nEYRv7mm9/zw0/v+OLl51zfveDm7iU/fXiLzpmrVcMQAmEYCcOJq/U1dy+uiUESy3HomcYT82gw6oraaRSWq02NIqFVPquvzLMnFCLbZrtivWloV5XQ8hew848wtDQK8+Q5Wv7UCwxZdGzDsEf5E3P3yPt33/Hd92+ZZ8//8D/+T7x49ZLbu1uGYc84zYzzyJwjyq2p1wZTNShbl+SjOA8UZq55IrYWkApehuWXfkI+B0KWZ6yII4ASxwmtsfrTdRiYY2CKgdifGOcZoyqM1WDAJ4sPicmPvBs6Ur2FL/8MszphRk/UDSEbhm4m5kbo/mFipWuyqYkhk+aJiUzfPfJwn/kx5cKszWgVRcBBgS+klBwj2C3Wrri+e8l+/xY/BtZa5gq77sTYH4CMMSK6HcMTDZac8dOJWUikhNlDThgDdWVQVmZGVVYkpHKVIBhQyaNSRMeJHAJx9gzJMOkKqoZVGKhtpt44rK4gJ354PzH4yOQjKipsjAwM/PiH7+ge9vT7R25fvOD25QvcqpIZzHkgRy+BPAcZN9Eao0ypBEVoPBUZt19y/OIgGIjMyXOaOvzsSSGKHmFlcVXDZrNlmkRg288j09hB9mjVYozC+1l6e0NPmCZpcsbIOE9Co509dbNitdpgjDTAtalA26Ja7/A+cNwfGfqO9x/f8/HxnomAagzN1QpdayJBAuw4MqLLrBmEWVwq9M8qJv5IeZcvs0HLnvWfuaLq7/vkXL5dXvxE4/iMep4ruRIAPyGN8uzLT4Lmwhx9/ohm5tTjUyKHhjfbl7y5esNX6xuqyXM6vOPwcE8YJ7Zty6u7Ld577j+8wyjJkhvnCDGXsRPhKruqKl+HlDzTnJjmDNoyzhNtbQRajoHHg2ecxFbnrr2lS4ahOwEKlWtu0g12VMy+J0wnxvFEHiecsjjjgEmUZRYv2HLEnAhlVpBU+gmlOkMliIlTd+LdDz/x+x/+mpwnfvz2R/7md9+z7yd+84/+tKxZw+HxfZnrmgU+PR64qg2ff33i868i1eolWllIy/BDEohzCWAqirLH000+L+owBQ7NlzoyAyQR+I1PqiLg7KqiSwAEgaMeDwd++vCRD8cOXVVUbcNK14SYeDgceTh1HIeRZr3BVnWBxnTZuBbnjhPTPGFWEWc1tdUEk9HZsmocbS3VvSj3aNpGfv4cAjEmcR7XkIOQIJq2pqornLOFQ7kIVf/8UVJqqXovZ6tBlHeAFGYhYPiRHCf8PPD4uOdwOOJ95Hg8cXN3R9PITKLRHhsUU/KkOGBSh0kiAbYIJuQcSwWYWcwEEoox16SsiHqR2Dpbbj954yUJ1vqC2EBxX1j+hWKzXrNqWhENR67LNM9YHFbXfPnVr3jsZvL9iQ8+MabIyWiG5prgFD6aYhyQUVrQL6sUpt2ijCbGiWwEMtYOcgyENJO8tBNc7cA4EooQQTEWSHLxonRgK7JxDOOeeZrxZYRMKc5CBE/FCDKIbF5KGGVQ2rLMCfpY+tYKsgG0urhhpCCIXpbnr+96huOJD4eR4xjQTvHZ3QqnEytViJRT5KHWBAxjlKuasxh3+9Ez6J77dx8EvUuB65e3kpAt0O9idRVFWYrC3s1JFULgH8Ps/vjxDwiCiTHNnOYeNXlUzNh2RV1ZmvWKzWZD13UowE8D03BC0+Cchsri/cQ4jfTDQJynsx1MZhJTSR/ZxCgeWK5BXIZdMXiVKnGeAo8fH3n//kc+7h94OOxxm4amrWiuWzSGSGCaBpw2qJCJIRV6sWDWn9ru/ew4456XYfQC8p7hrp99y8/xkvNxyTA//f78/DuWILdAOkuieoZ4nr49dd5Yn0JsT4/AQMhgUsNnu8/4zd3XfL7asj+85eHdOx73j8za0NYNr2439EPP99//RF011FXDZlUxkRnHUIKgoaqcuHqQGMaeyQfmOUi+nRObVYUzkFJgf5xIxdz4pr1BjfCu+wgojGq5SleoMdOnI8PpntEP+LkTr8PiKGGs+ZkP4xmMKXR4cmEGa8n+CfIQ3v/0nv/w7/6GU7/nb69XnE5ArvmXf/GnpODpjkc+vvuReZhQQXMaOj7ea8zYMY0eVxnuvtpibQtZYVI6b+SqZEaJSNaaXCBBaecU8eK89Jjkzoi3oMz/JXXxQ3y6iJTWZ7++lDPRT+wPB95++MD74wnXNKzaFldVzCFyvz+wP/WcRk+92mBsRYhSuRhjqKuaaZo4nU7M88QqJ5m11QqrDCTDuq1YNWKxk9GkrGnbipASc4h0/VBk6OT6GkQ8u6pcmdldIPqzrs/z0yqR8dLLvlxDDUTvyX6CMEKcifPI4/7AqesJMXM8DaSYaZuWVVtjjMZFyH0kpBGTemxK2OSJaVGmEeFMrcA5oTTFbIipIuQnqjH5yT09v0GpEp0158o8pVwq3su92q7Wci+clUFxFHPwZG0xteXrX/0p9f2BLrzlhw+RY4jcW8XQ7ghtTZgSaRaaf2U0RotodqUj2rrSt5vJKdDUlugnpghjnEFDbRuyq4nKMHkZJFdpLnN3GTFkrsmmYhhGpnkieBGjPxP2cn5OZMrSe40xoa1FG4sYB4s9UyyVaEaBVuiFEJU10tQSD8qhH7l/3PPxMOOzwjjDm7uWbaVYM/K4DzweM21tmBIoz0KlJsUsZt/AQ5E/CyngioOQNrq4GImjS0oi16YWjpRa7u8v95H4xUHQh5lhHLg/PFDNCYtm1zbknAgxMIwjp+7E/f6e777X9P2B3/7Jr1FFDupw6hmniaEfxMw1yLR/KELJwzDw+vVr6rpGIcSEefaC4BtLBt7+9BPffPN7Tt2eZGBzvWX78ppm3bLabLDK4JTlsT/QTSMWx+Fx4vFwJITF7PL5eT27XAtjE3mYw7ON6gnr8+mry5Oy/HFp7EnGVoJZymIW+enPeBrAfh7U1LNX8ua9tgABAABJREFUnwbARVkm8Xzuafm2dVNzs7nipvqaf/0X/4I/ffU14XAviyonumHkNAUO/pHdix3OrdHa8vHhyDw/YE0Wp4CrFd/88MAcJuo60DqDM7KFaWMxThiVbbvi5cs7wiyKDutWC4klJaZxxEXLl+0b1lXFVb1i7kb62ROmiePjPUPoOYUOH2ZSigzHmdppzq4G5TBZY7JwMHMpPwzIjGBWhPGICol10zAf4eO7gT9885Y/+0e/5atfveD22rC/7xkOBypTkZ2IGo/9QBojfzl7fnro+Mv/8Nf8D/+Pe16++ZrPvv4LcraQNSp7xGtQkY1sPimXfjagsyFrXcgXRc9QKUyS2UKd4SId/vx4ppOZM6TE+w8fCDHxl99+RzfOwnCOidlP/N233/NwGpgSnMYZPwVsDKxWjsrIAPXQd4xjx+3dNa8+e8nty1f8u3/7b6Q6nEZev36BM1tSCqIYU9es1ytCzJz6iWEYmGbp7VfG0FQVV6sVq7rBaiP6qpkLA/OTMPgUIX26zjXFRs3P5Dij8axry1w5QlISbpXC1S22bjGuxpia2mmaWrFZLSLaE113T/8wkGPGWE1VVTJBoRWClVcoXQnhTmtQVuzCcsKUqmKBR1VJRpyWCo8Cu1tzuV9aKW6aNZumRRvI2QFiI9SsajbbLdfXn+HZsh8r/sWV5WMf+XfvTvQRBt2QTEtFlnGglFFGU1crTHCouEW5DS2JikRlMynO+Lmj7w4kMqZZM2kRudB5RdKP5KTEPT5G5gjZNmRT8/DwyDRPxOhRy97x9xw5JoIXY3SjL4LofrHkUomQZ3xO+Cy92rWxbDXUStG0O2oFdV3h2xMxRQyJ37y+ojXQ33s6I1KD17USw+kAfTDCAo6JYfL4FKmyZX5InIYR4yrW2zVX11fndaVL4oZSTFFIMlYrsIgH9S88fnEQXMwj53mGkMlZMU4jPivUOPPw+MD+sKfrOx72Bq2FiBJCQM+zuEVMM0PXczx2hODJOeO9FyWZENjtZP5j9hM5ibfc7GUA3s8z/djTdR1Zg60d7dWGdr3CNRUUbb0EHMaOHDriFOkePV0v/oNGa5yrnp2XKkGkfPJH67lzjvhJb+PTf6GefK4kWWIlaBoxwRAvKmBPN4Qnv/4/i2M/Q2wyRQQblAbzyRtvqhVX6xu+uv6Su+0V66amf5QqwTmxd1FzYB4GxqEGo2jblmEK+JjoxxnQxdYnQxayiVNi7hpIIrOmNM4JczAncScIIaCQe59SZHQjkDE4TJCU7XTqCPOML8SPIQwc00DAk1USF/TMz2TTlkBiEN+1pVJegkrM0oOqjMKgBCGNmaoyrFqHnybmcWSahFmnykZESOQQGNSIjok0en74/e9JWdFub9iuXuJsTdF0Kz3AJzJpT29kEiw7Pekkx9I3lMp1qVieHkVg7AndVynN7GdOpyM/fXhPbSy7ZsWqbZj9xOP+xP54Yphmfnz3nlY7KhR3rFEZtm0NCqw17LYbrndbrnYbge+CkCJiGU73fsYWm5+qcrji/5YL6aFyjqauWbUtTV3jznOBSyVVAuEfWbPLfOUFCVlgZentig9dliqvcqxWLbs5opTm5uaKppZ+kFEKyvC9NqVijpnsNDhFyJ4cE34Yzi4C2mqwNdgGvf0MZR1m8TzMWoLg8nmizDOX9kTKZ4sr/enz5SyVLd6SxcHEIKMTVVXz8bHjNEaUbblbtRg7c/fYMeqI0x5cjc1gyxC8M9A4BdqSoxg0W6l1SIRiGqswWRVRBkvGoJLC6BplW7Jbo7LFqJrkoyQJyggJRxms8WQ/Y4zIvsUQCQKkPlmGqhTJxZtQUeZ2l2RHHkBViDWmwI5aRazK1M6y2azJxuCtEwWX7Lnbttic8FrJvSbTOMUUFLXVjFHGZmLKQhRSoGwizxInjvs9SmU2mxXaCAs5n0lrTxLI8p7/8zvp8+OXB0Ffem0lCMYE8+MDPj0w+cTv//ANH+8/cH94wIce7ydm/xfoYcTPgdPjgX4YORw7Pj4emKb57J+VUqJta65vBhkoHTtizJyOA33fM/TyZz/09PPAzcsb1rs116/voFJkwxkiCynR7z/SH3se3j/ijzJ/GMtg97pdPcuENLJ4Wa7bs0bckz/OATI//+Inryz/RqGwGm4qYTGGBHHKhIVwyCe9ouXXn1/l+c9Xz19YeotGyQZSm8v3KBTb9oY3t1/yL//0n3O32YrsUE44Z2nXazbbDaMPpHnk8LDHNBU3t1ckpdG2Z995Zp+I3uN0IplEN444XZOxjAHEqdyxW9copdg/7OmGgRA8qEDwUTapLNfeKsPkJ9AzP4VEjjK03vcdfRo45J6ZmawDtXVYtMCGT+HglDBZSEBKIw4NpDJXKAQGpzW1VTRG0VpDXK3YrCvWrWb/4ZH944m+74lhIOeEdhozZPIciWngNAbGLvDX//4vOeyPKKv4s3/0r6mal4Ajh9JPKYQZ9Vx/ocDZiaSCwOo547NFZOHVORD+7B6zDNELe04bRfCe0+nA333zO3JIbNsdc5B+4U8fD7x9+45jd6LSLdt2y6bdMMXEi6vIphULstVqxasXt7x6ecvNyxvapqXvBsia4EWqsB9OrPSayra0TcMwTFgDKkcMsFk13FyLC8V2tcY5e+53AmJlk/n53lNc6hc91KyKkFouX0uIK7kBow1t0/DyxR3bzQbnKn7z6y/ZbleEqcdo0au0RlE5IeORMq3Z4NuK02EvaNTDPfeHk8zMOYepWkyz5q75lXiKaoXKBlWIPxfh+tJHVAqRDcvFud08C4JKQduIaECISdjvSmMVcm2ahv/f3/4BXW2o2h2vbq7ZtiOH4wOrkOgIqCay9LCOA6DBVRCiJkVDtpX02LK0HrLWZOfArUU+bBpJPklbylRktwagsonG1qRuJvpEVJbq7gvMOBCnkXi8pzKwWTliFGPdw37/ZO8oRKeUSVkSiYXwuKzTnDLJIGScBDEEsksop6grOf/dzS13UwfRY9JEqyFMIwclZCVDZF0ZfFR0VWI/i4vGnBIhiKiFsqKRG0Pg/sN7cgrc3FxRt7WQ1ZaWSIG2c1Ik5aUgyP8V/ARF0SAyjiMxgkXROMt+f+TD/SM//vQDp/5E0hmcxhP55rtvaZsVlas57I903cD+8cChG/CLa3EujU6dCWEmpplhjAz9xI8/fODh8Z7j8cD+sAet2Fxv2L64pmorspbAl0LC54hRVlhCVpGsIhoY/ARRs9rsuL5quS72H3AJWk8rrE8rQfVH/74IaYnHW8qL9qE6q+A0JtNa2FSS8cacGSPyEcrvLVDpWXfy3C/5BHL9I68KQyxRacmqJvNMTpo0KWpqvrh5hcmaebg4SMuPShitaGrH/WFPPGT8tIUiQffx8cAwDHz8OHN3u6XWimwVOQbGEDgNCescdZ2BqvzSRPCTwOb7B8mOtcZUBmcrjK5gGHA5E/qi8KAyMXkOaeRj6vEKUIbaQGWyuC08uQnaKbQTONItbMCUJBIpcK7GVg2mqtlsW279hrtqzc1qjUuGbjgyjIMIdxerpjkm+j7hx4iqMlfXK66vtnRd4O3378nx31CbhpdvvuTm5nOMrmStaQelq5TFyO8yA5UhFUxGLXAhBZ3LPytwZUbwDMdf0AlFgpT46cdvcSg2qx39i88BmKLjdJy4//CR/r7n7sUbPvvsa9CG0zgRU+DQz+Sssc5KBRg9m1VL8jsa67AKop+IoWKeLUprmtWWzarlZrehv7km5cT19RXrVUvT1JiikrLYqoE6BxNrn+NQMlt5SfaWjxQjOUTCdCT7DvyI0pmqrvn6669Yr1fUdY3RmWns8fMoslxaySiB1mgF0xQ4HE8cTifev/+JYezp+46uX2DBjKpbqlXkxa+1vHcj/nOS8y66vKUn+/RJU2XYX6lPcmPF1aZl3bT4IOa6WmkaY7m7ucZub/kPfxjRKdDaTHQ1TVXx5ddfcj0H5iIDeXh85PHhgeGn70kqicCErkA5UC2eiqAs5FhaKoqIImSFz+ByQqdi+Owc2K2wZY1i1h5PIKqErRpq7TD1GqzGZE+tI9k57Cc7ntERqzPalGRNnjCBRlHMIZ3vn1IGlSM5jnJNs5a5xSziFWrs0MnjcsAYsVty2qBzJqdIW9dko9GVZko93ZTopsu1T1ESyJRh6jrG2jENPW1bU1lzIfWU67JsbUsl/3+GrC3HPyAICrzpQyCFRERRZVGdOHVHTt2R0U9oK27KyhkeT0cRcTaO7tDTdz2Hw5F+mgsxoAwkaIUPjhBmQpjxc+J06vn48SP7w56+PzFNE/W6oW5rXFthaicspTJzkFMim0RWotigjMJURmZQUFR1xXq9JuT4rBJUXBa4VHxPXQI+BSovl1UjWWmtL5VeUBe5r9YIFFpiICU3IJTm8vnHP/nRZ8kt9QlsWpBB0uVbyhTa+WZ/GqwtFbVuZJg9ykyTKoE2xkgMInFkjCLME1OQK9XudtSNEB86L1Y769HhrJU+TnECmedwhtpSEqsYyuIOwTMMA7oInvvg0drgLHhiGZVRGC3VnCfQp5kuzkw5EUPGGIWxmbMA83JuRliUMoC+JA7l3yjAGJx1VK5htW7Y+hbXKmrnUBHi7MkxXrzIMuSYhVqdFDopjLZUdc04Z9JhQKV3/Pjdt6SUqGzDqr2mrjeoEuRyzlIVKlEjOd9a9eld+ePF0uWrXKBVlopQoIPT6cDD/oF3H95jmyucrYiqAmXJSfPw4QGtaqp6Q7OSXn1jMslnau1wVQso/DzjjKGpnISBFIhhAjbkFIkxoBDx+/Wq5Wq3IaXEdt1S1xWVE9Hkpy7zi+t5Rp3tjC6nU/4tha95LnmjiB5HEV8mzGRnsdZydVVzdbWjrmvGcSqeklqYySUgxRjwMXM8nXjcH3g8HPn4uBeB5XlimDyhSIdZ3WJy6WEbXeT41DkBXbirmssyWm6KBMfnABEK2rpm1daEINfDKsOqqtlu1uhVy+RPAhEmSFrMwre7LfU8E2OUsYsBopr5MHyUfp0XMgvKkc2WpGo8FcWXgoQmFUsmYViV+TwtLvXKWIyzKJWIeSYVNRWVMlZpnFXYpsEkjckTGU34JGnRKqF1vkDGBaLU+jJvSUlszoN5KaCSLgbmxZIsy4iHSh4IxGgI3he2sYxmqEpRO4OyFVerGaOkmpuTAOY5LQNHWZDI2RNmkXS7yCNI4aHVspE+fX5+2fGLgqCMgkSiD3jtOR07SImqqfHBE2OgnzqSgs1uw91nL2mahv2p5/50Is2JsRvxs2caJrRZFrMWll+Eccx03Z7D4Z7HhxOPD0e++eZ7pmk6j1esrjesrnZQW7JTKKsKkzCjfEIpyeQimaqpuHlxx/HdRFaaq+sbNpsVc5ifb07qUkFdxidyCTx/vA4EGSSudObGiV1OQNNHgTgaBTe1ZmXBqsyUpQK0OlNpqQYCF0hTlXLe6gvjMZZo51QsGn0yD/c0fOdc5uZyYgzpCYyqeLH5nBebN6zrltCfiDlhrSGEwOl04ng4MAxjmalJjMPE2/cnfvXnG65fbLm63vHTu3f8p67jP337gcoZvnh1LZl4zqLVpxPRwTzVRRnIkJMI8FZWFU3BSCayWlm+eH3Nw4dMGL3YzqRISoGP057HNPIhjDyeJiIR24BbB0yMz668NgZjbPETLHCcNvLwqozRhm274uX1C7589ZJNo9BNRmMYO4/2mbXS1G3NKSemHEjzLHNQuoW6gXqN1xXHMUAfODyOHA//L65vd/yr/2HPn/75P+OLr36LSgLHRpXIulQ7535EGatIMmB/fjYVBT/9ZPI0IUPHMRbtRQTuM5Io9N2RH3/8jq4biWbN1fVLmnbH3We/oWl2/N3/8b9z/9NbDg/3ZA2nbsfp0PBqV/Hyesf1m18T54mPH/aQPbVL1AZ8f2Ak8OL1G+mx+cDU9zjjeP3yBU3ViJoJqQQQSZpSNOQoZszGio1PgtKre35ay8qU2T25PDlMMHeodIB4IvoBZbc462g3G4y1hBjpx4H1asV6s5aeXflJ7z584HDY893vv2N/GjgNI900SgKoDdMUyFmjqxU3L77k5vUXrDZbtBWt1NKSJES4DMk/qcSXCpEkogHP4FDF1XrHzW4jPqc645zjentDc/2CSdfM0zco3VArwxglId9st8RphBRZ1YYtIzvV8+Fv9/SnPWk/ESIkNMGsOWbHgAVTFRJaha5btKmxbkecJvwcSDajrfj6mTij8GQGdOwxYWDev8UZi7WOq9YJS9l7Ilnsy7icvrEJayOmxMZcyDBaZ1AKFRMxZVLIJCW7rSFhEflCXWyXfMiMhz06zkQV8QmGyfPh8cjHQ8fDaaBeV6zWNXe7Na0TP8Uf70/cd55ujvgoVaVGkyIE7xn6juCvSJW77NVIUbI0kmJOxRrtlx2/fFheScRNWVzjwzjx09u3hJhZNS2rZk1QiaquMbXDNnLj/OCZzUyaRnICXeli0VEquOINN/tE1x95ePjAh/cHDvueYeiZZ5FOU0ZgMFcbtFWgISSxD1no6XpZzzmTNRgDzbomzplh7jBTxhdCzvm0kJv85DTLpRWobtHFX0Yrlu3NKhGvncZeoBnr2BiHUlAp0MhiCSUAjsUbz5BZW5iTsEUj0tfTZNrCbok544Nk0E5n5pTxqcxon8OmaLLG0h97ioArpXjz8jNe372iririMZLGkXF/YDp1+GnCe8/kZ6ZpZPYz8+zphongAxrFF5+/Bq24Pxw5diPD5Pm472idwRpNW8lcmR9H9jGVDduyPxwYp5GLNY1i6EfGpsZHj3UGQ8aqBNkQIwxTYsiJmYQyooNY14r1uhKDUnVRutdltivp5f4JJVrnks+bjFvX7F69Yne1I4SOKfcYJddp1LKOGmtQek0TE66NrDci6htRNKua1hmqFyLc4Jxh7o6EKDCcMRrrNCw9Sxbq+LJZqrJKElkrIEkgfCKf8gnpFRUTaIE+z+szluBRIof3E8fTI2/ffc/gAy9ff0U2Fc3mhs++/hOGoWOcevppwI6OdbNiiJbjrPnxvsOkCD7SrDfkxkGBhHXpaS3RK/pZzIJNQ9s0IqDuZ4xWlyF3hCxnrD4n3Uuoe35iLHndhT2awaiEMYnkIjEpYnYFpZDNS8Y8LDe3tyLOrTXv370XS6AwsX+8p+97TsNAzFHc070mxszgM6vdS5rVhpsXb7h99YbtzR3WVuVcZd3I+KsSghdczLOWalBlTCGdPS9wFVHVRN2gdEY5Dc5BvaH3kZM/0XVHTK5w667IS0pVjbPncYBmveFGveHrP/3HHB/v6R4/MIwzPiU8lqnrmPu96HMqTTaWZGpUvaa+dVztNtSm4uQ7MiJ2/9V6jSUyToZ3xz2nGKmcYuU028qwbSw6QzQVPmd8CM86LSlrYlYyfxdi8RLMpYWkmH0gZkVKiiGILZ7NE7Oz1CkTUyBFIVSFeSD7iTl6ugDd5Hl/7HnoRg6DZ+ciTZOwZHatxWmYfUPKoPEcxiDLJSaiEUbvHCOTDxjvy/2RZzIXicacc5Eg5BcfvywILgtZHmt8CMzTxNR3rNZb1u2W9WrNTMRWDu0sujJUdQN6FKZPp1HIZH+cAznKG05J8sQQxR/tdDhwPOw5ncZSBYo6gF1pjNOY2qCNNNhjDGU6hWJTc8GEUZAN1CuHN4k5DMyzkpv+FF5TTyTHnuCKy5o3SoKU1Rch64TGIoFumias0dQKmuIYrRWoYg/jE0wJvNxPDMIEA9BJnBksUiWurfzWkDJTuaFOy+aSUi7ZjmymF2bUc3KNvHfFy5sX3F3f4oxkfsl75l6ECnIQ3cCUZLxlgax8EFk5leHF7S3j7NldXcH3PzFNnn03EWtLUxlWjcyS+VnWQirS0cfuRIge6+RiKgXjODGOwvTVVnhvOs5CpjAwkZhzIpDQBrRRVJWmaWtiej7WokllFaqzGbLhiUCzSbimYn13y263Yxr2xL7H6DITpoXE4ZT4qsWscEk235hEMs5YI16YqxWubqhXKz58n0sfSmBP2XQKDJULG7islwuCIOQZvcwu5cvi+hlNPS1Q6gLplPubissBEKNnGE58uP8Jj2J1dUdjHLbdcvfmCw77e9LjB5E0nD0+acaoOXn46aGjVlCpzGq1RmcHYWQcPcaKC8HZFDdFVBaCSFU7dND4JALJMvIgcobx0tC+vN8/ln9/gtdLUpnROpFMxliIyeDjpX+ojRE/vrouEoye+4cHuq5jHHuOp4NYDwWBWU2RbwwJuTbXL7i6fsnnX/+G3fWtBH4lAVDrIrpekjSyDLuLrduyknLZH2Q/+ZQdOmfHnKsiqGHR2hFMQz+OPHY9Y3/CqIZq6DkcDxiV2Kzbc18+pIypW9ZVzWdf/5b15ppHW3M4neT+pcg4dMyxF8f6LJyDoCtMmKlvv+TFdsVuteX9MRGjhxT5arvF5sSRyAFFnxKVUzS1Zt0YNpXM3gbtmHJmmBc1imUfkeQgkZm9jDulkuQqpZgL6zRlhY6yxp3yzAFmD/M8MSeF95EwT0LGCTOHOXOaAg/dyGn0DD6yXgoDBeva4oxiCjXjLPqf/eRFVKL8vphlzfkUhf1b5Aut9JJkn8xJ0Ij/4kEQiFrjFfRxEnp8znQPj2zbKz67e40PDce546N/QDuDqkRnsDEVttHkKhXLmkwePDkkQvD4Yq0Rw0zVOCprefPyM9bNSHcocGjwtJsdZmVQVSKnAShcu5Jhx5hIKcigq3MCberEzWcNISSCT1R1Ig/PWUNaLZp4l0f0gjdLT89pqA0yv1Rw72WQfu8jKiTGpAlaho6LQ2wJzEXsOV8yTIWi1uIFlsKlFzF46f88HWD1Sd5PbQSGjDkRklSiqrz3lKFXz3sZr65vudtdAwbTrKlSppknsrNUqwZT1wzTyIu+5+Vx4NRNXP90z6ZyTJ1YK2ltaFcrnHN470FZTmNkmAIxZpwxVEYzp8Bp6Pjh/YGYItoodlct1hqMhXGa2B+OfP+jZdOusCjSMOK0IqtEtFmGzEPEOrCVYb1tqOoNs8/Aj+f7EYh4Aj5Dla0E0nPgEcq8rip2r2751Z//MzabK779m/8VHwZCms892qgUzmRqo9hVlqQsKSmmbjqjFCrOrG3N61dbvtyuUCnj33/krf1LhuN7bj//gma9pd1eoaLMEaZzpS6VsgyLa2ySiklnAI1Lz59SlZJ8FLRlCYbnpWB0WVCZh4/fM44nlNHc3X3BZnON3dyyWW3ZfPYZfpqw2jHGxH6YGXzg8XjgatVwtVnx3/75F7QOLDOrkFDakJUtjiqKtm1QxpGXIQ8lLvfT0JNCYLW9EhkxJVVxVtITT0hv9WkcXJIDEao+L08qI6zQbCJeJyIZbWVEYr1es2pXWGtl5OrhgY8fP3L/+EhKEW0NzWqNqxtSVqKB2vdobdhdX7O9+4IvfvtP2exuadc7rBKGqjXC2LY6Y42VimPO0j+Mpc9UpPhylk6c0aqMh7jzA5ZS5pv7nvtQYYwTxwsTaQ6PPDy8ZX94YJ4GDHuO6gf+5v57KmfZbLe0qzVV3bDZXdG2DU3bsL79jNvbV/zTP/sLHh4+0I0DH/uejx/u2T/seTyemHxkDAHbrGk3W778+k/4/PoFm6rBfnciJllvN29uCNHzjo7BKCbgqmm4aipu2loS7QzBVpgEw+yf7/NZ7oVSCp+kGvRzQGtZiNMUhBBTHDxiDqQwwHDAEfDTjE+akBR3VcDkQA4z/Zzph5nT8UBbObarNV+9eUNTGRoHlbX4KGzR0Qup7DB4phjxOTPnRNRQrSpsYzCNJqaAqRSu0uW5UYWtqzDVJ1nLf+b4B8OhMecCCVnOgrlZ0dQ1Uw6ooAuEc5HkUVoJrTUXyNJJQMwp4b0EwRSk6thstkRvmedMTKk4IwcWJoPAFREBZhZRbgks578r+Z3OGGnqWi1Yt1H4iSfRQrYq0QktlUQWCKR8WQaxy+tlDyJmef8xJkJRcU4xYUMiFPxKUTD2AgOl8oEqD/8yX1N2jJzBl2B5GaG4/J/M2fJJn8kwF7jWPK1ugdpYnDLkErC1Vhgnhp+5qli3DdZqrNUoZXGmYp4C9XZF09TMPsqc5jRDlLk9a3RRZshMQeYEY5TqzRpNXVsmLxmZ975U+jIzOowzD49HwpSEIRY91koQHPxIiEGutZVFXa8cxplz7+9yx6TLdBZKJj8tsM733xi4fvUSiBw+vuH48I7YH1DZy1VPSmYSAZc0xlRCda/1WXppc/2C7c0tr958hUpa7Jm6gezh8d2ex33P6mrD9atb7m4/o3It1rSktPSYjLQQll5TId4ok7Hmacc5X0gG5fOnwg6XUxW4MfqRaTAcHt7RNBuMq1i1G4x16KyIvWTfpCPklpQs2UE3e1Q/cn/ouVrXXG1arBVWJFqfa4GcM6mohOz7iZQylTIiOBEz4+xRRjZCU5iVKMian41+6FL1LsmabCXiOpCzzP8GL0lVtXZSeRbhfJmRPBVGeqRuakkQlSjxKB/ph5kQRRHm+vYFq6s7rl5+xXp3RdW00kNWJfgZeR5FMUdJK0IX09r8XDhfJUkIbCF3WXtJWjIwFdhVRXBR9DXHGLnfD+yPPTpnaqNZVwYVAmGa+NB3uKrGOEe7XtO0DW3b8PJmx2q75uWLW+oaxmlkN03cbrYcbjvuDye6ybMfPcHUuLrF1GuGADHOhGRQWmGtYlKKIUbuhxP9JHPXVaXF/7CpqCyQRFYvBhlderpvGKexlUEpjUsJpUXI/ixOXdAKnYW8JuoxmikGfJg4HXuOvacfA+m6oq00q0oCFsCqrllvdqzaNcZaUk5Mc8C6iqzlnviYBJXiwrjXxmCcpW4rXG3EsioqdBmMV1q6CfpJVfhLj3+AgLY8kSknrLPoOpcLKOa6zjqRGsoUJmkiZMEAlZbsUiuNURrTlmkUrWWgupiG3Ww23FxdMfeZofeEIDpzc/TnweQzM0ohJ3sesVg0KOR1bWSoNuSMjpm6cqikmD6ZKjeAK30ASm9QAAJhhWk56TIYXqqIFJlG6aOFlEBpCQY+omM+9wy1UqIYA4U2LD8rICwsBWfXl+XjMhb3JFAqWFymtRJR9+WOSB80/2xY3mGxaFIMKBJKg3Wa5Cw4sX1pKkvbOLSqqN0MGex6hV21DKPn1A8cjyeSj5gMtVUyZK8Uh9MkivFkrrc1TeW4vVqz73om7/HeE0PCaIurLCGM9L2nbz21tbSVaCJGndgPPYqMMxpnLVVtabYO2xjm8Mm8T7n/Rqkzm3aJFpdPE4bM3Zef0WxaTvcP+CEyHAa0mktlrhjjiNERR6aqVxhTUVc1OTuyqvjiq99w/eozPvv1bzCVE0m2tw+8//E7PvzwI998/3vWtw2f/eaO//Zf/ffc3Lykdi3ZI5P8yoqcm77MNeqk0DZTmecPac4Rsi73/6kM97kXUWJkIqeJOUUePnxPvdoWTdENFKWlcZIe3ikdgWvyqsXVawYfCbHnu5/ume6uWG13NLUMj6fghe6eMzF6fPIMEd5+eCBFuFpvRQQcGPsepQ3KWKrKFfd4+XgiRSnvXhXYWmvO9kYkSJ4UZ8ZhKExlRbNqqZsWpWD2Mkf88PAgQhfGcHW1E06Cn1HaMirP8NAx+Uyk4s3Xv2V7+5qrl1+QTCPSYQqZKzQKa1KpBKUqVxmsyTgr1zlnGeLPAEZhlMI5TeXMz0Y/fFRIy0r621olUJGP+5HDccJpzaZ23G3XzNNEdzrx9qe30jPLCWUUVV3Ttg3/4p/8Y26bL3jx8k/YrbUIGOTMMESGMfJuf+Khn/npOPLgFT5porK8P03EecZ7TVVL0vgYM/tx5PuHjxy6I2keqNoVbe1YrVtqk2SMwRvSDJV9MiqvwDWGqjGl2tN4H8lKMU8BnyIxycqMqaizGNkTQpcIs+fhOPDj+0c+3B/JX1xzd7Xm85fXpOTRKG62W16+/ozd7oa37z8weE8MI1XriCimEOimieM44aP4ZWRl0M7hmprVdkXVOFyt0cEUK69LH9AUVDD/l54TVEDlHNZa/DCyWW9otxV3myucrYk5Mk4TsxdvKo2W4eVE0XlTpFT6McaUPiBYY876g+SIspbJR7797js+3u9FO9tZtMr4ORODIifZVEFQx6XKURgJVDkRfETHSNSqKEJo0XI0Gm0Mz+JF0do7Z4GUaldlzmqBhRKsrcGQiP7EdHjH6XTArm8xrkGZtlS+ov8Yz/3DXDa2hR6uSr/lWZvkHNCEfJTPgZAlQHLZ5OUr6dwLFEm255vqInkUsyf6njSeiI8H0jiR5pkche07j5OoSOTIetOiNxtU03Lqj0xDj0oBZw05RoZxos5i49MPHqMzVmfeP4hgcSqlsrWKnB0hgPeJbhpw1tDUxaVcQTfPeBUJLqF2spm2dUPwE6pRVI1YpwzdcyKTcxVVVcmQexI0IBmZV1JJGuQqZzH5dGBfXPGP/q//mqu7F9x//yN/+P1fMvYHhu7APBtSyvQ5MlVBWMvG8MWvfs1Xf/Ln/Oa/+Ze0mx3VZgNEWSs317z48hX9oePFX/2ex4efeP9X3/Jv9v8LVy9u+M0/+xdst69o6h0m6AukyLLGZLPM+pNoQSRns8AYT1CARWJNne+9BogJ35/Yf/iRGAJrt8LUK5xrWDVrZqXo+wcOh4/4qeFqu0HrRFaJ+8dDEQ5XXO1a2eTJWDyGKJJXCfqQ+bf/7t8zjp7XL19zd7WjdhU/vH9PzJCV5sXtLev1ipurHW1dM47P4TWjihh10XfMSZG1F7Wp7kB3GqmrmvV2h60caIqWsHzEUtpro5mDKD8N40g3TExTJCnLyy8+5/rmFW9+9VuqVjz4nBMfU6vKHqAEBjXFESL6TNQZq6CuLMYmvF9kt4V1XgB2CRqfJGOVTPHhKVqWCIPez1H0dpUYvVY68urzz4ghcHO9Zr9/oOtP7Pf3pHmmnw/87V9HPvz4O/7wn/4jlRJ5uqvtjqpucVWNRWHmgXTa0508Q1R4WkIQQrH3gdx5UpohHpnHI/uP7xiPB7QfCGtx8tltd7QOkvdMXSdD/9VzBS2UKNSkGJh8KibkqqgJOZSqmGcvHzHhrKLdVmBaZpcI93vGmDj5yGmOXGnL5uqWaf5ACBNKaYY5M58m/urHR6Z5xIeJl2MiZfjw2PPxMHMaA0NSaGOwzvLFl19w8+Kaqm6ZQ8RHmQ12iL+nVUY8amMkTJFp/q8wLL9AnzlnjLVUlTj+EhH1jBwEegOMMuWjwJTLTr3AQupSVQoKKQ9KypnZBw7HA8PQUzcVrnZSFheICvQTWZwLfPMUOsylNyHMz+dB74+WyU9eyuoJHPkk6miEzSil4EiaT8TxEVwrQQiN1hXGlOpsyedL9ZhLLwjKJl36L0/5EfnJ+5PA+RwaVeWTsx0TF/bg8xiozh6OKWeBnoMMJ4unoogj+xAJUeZ6lAZb1+iqJjvHdBDiQY5LEDT4JGLaIm4uUTxBEdEGpRfIq4haZxEJ8IV+Z3UU6TprSDky6YDXiWrdUFUVrqrx3UzWshX5MTD2z80xlfg2nNeaQqOK2/yi5q/KBVSAtSKw8OLNa5y1HLoHHj8qxmFkt13JGokB5RxZKfox0GyuePnFF1zf3eHqhnyGwhSmbjDWUTVrpj7iGsscBsJw5HR/5P7HH1DZoK8Urd2UTEdJOwFIZfj6+eDZE2rCGY97si6eVP7nP1Mi4ZmGE8Y4xu6AQ9OYWqQBc8B7y6Lte8mgIn4eGQdFd6qxJgvZyRlS9hgS2kg/yM+J/f6R42kQUYFpoqkqfv/d9+JQohTjPHNzfYVzIqPmfTiv4aUloKVjIqtfK3JxHxD2IaA1tsgZppxFdCFE8exLubgdRLz3eB+YvWeaAz7BenvN9e0r7l6+YbO9xjiHNrYoyqhzu0BlEbZYPpKSZ1SjykycJpl0RmKMLl6D537+EwY5MrrktPRwxYFD4UlnBZUx9Bz3D3ysRVxAaY3KUcZ4lDitxOiJKXLY3zMPHf3xQKMNlTHsNlva1Yq6aXDO8tgN7O/3HEsQnHVDzI6Ui+Zm8oQwEsdHwtwRplHUfrRwJNp2xXa7xaaZiCI7mdWuilD4eftQiYSMGKQs41mLY4Rh8bqUAX1jErrSmFqjo4FopMouXpceqeKsqyBbYvR0o8d3Mypo7k8D4zzh/VRk4BSPvacLgTlHjJW5TmsN623LalNja0VSiUQUuTudJKE0AoOSMtnkpfn+i45fPiwfhSV0oUvJUHyYA2GcZZZESXO5tjVt1aBIRSBbVBVUKn0RLcLK49AXaw+xJvE+0EXP/f6e4BMvX7/Ep4w+jZIlZ4tWjmVYQCsJwLn0H0VTsEBjJHLMBS5UoAwxiZ7l08uzTGwtsORimhNSodwmGayurEZFITAQ9qjwgPb3DF1NUPJxc52pXCMWQGXzS0gjPSfOqhpaczYjtma5BUvwk8AX82XfOlPLKYG9/PvlgV16jk/Pq24aKlcTgpeZrmRRxpFUYM4zD/0kFjmFemitoW63xKYhGE3X7en7A34cuGorZqt57E6SuAC766Y4OEem0RNDJKXC31BiLxRzIqkkPdQAKQZWVcXVtiXZhncciHri5vVdsSzSnLpHYR8Phv3HiYd307MAH+cTcTqQkgz/L7+rhENJHBKQtUA3ZczlzW9ueP0nN/gq8M3frvk4ZP67f/bPWa9WxJAIOtNPA//23/xbtm/e8OU//jW1NeQk9j5YXRJBi64sbdXy6//Lb/hq/hX/pPsX/M3/59/y8O5H/vp/+V/51T/5QPjV53z5J/8UrSpSlimqxVpJ/H0cT49lQiGXDVctOpzPfCVls865vJ4j4+GeMA58qDbwwrNy0mdqG0vbKvpTBxl8iGgCVnuB0NTMeAKjZmLTUF/vCgIhQTOESJg9fuzYPz7y3fdvWa9WGK355scfZYYL+PXXX/HFmzfUTQ1JmMBPD6NF2SiRhYGqNcq0TKYBGpxbYWwNRjMXkpk2AunOcyCGzDRNTNNADEEMk1OinyOmavmzf/zPubl+yW53i7W22FEpKmdlXCrFcwBThakr11vgFZWlWlWF2ntRbYpPnspP2D5k1jawrRLZaarViqwMwzBjjbjovH//Pd99+w0hJu5evGK1WrO7ukJr8SjsTx0xBmIS4shRKT6iUUGUUuZ5FmEIA5vaiiVWP9BHCCiycyi7QtkG12xJZEIO+PFETgGFp60rVq7h5avXfPHFV/zqqy95/PE7Eb5uEtZEDp9UglkHkpoJORJVIOsM2uCqCmtdgUgN8yRwaWWBFmoMVaq4rhtWdY2uHamypJIgBG85nTR/8+0JtfPk1vL2eGAeJ/w0c98FsrZ449AqYOrITdOisCjt2N5aVncKe72YCpcepgVtMhhhNJso99Xt4Hl0//uPf4CzvPRiYkrM3jPkkX7sxVQUzW63QU+WwzwQ5sDAKLYeReXBFtmdHLJUWymSinGrDGFmYojMkyfljHGGzW7NppsIGI5zQPZsDVmjVJHvSZFcKOrSf9DnXmE6U9Uks1ZaoZ74ggGlD5DOQUQgTHkYUpbeptWQlcL7kRgmxs5zGKCbLNM0imp5bem6Ez0d4yiZsxBhtBBZlNiU5KzQ2vLixS3r9Zrt2hXIM5/3upTyz6q7p36Cl2r2iXjs07utQBsr7hvzTAqR6CPRU36/CGDPOeOTJusGdFGjCB7vI/vHR7rDkTiOtFWF05qUnLC1oifnZag7s1pJgpKVpnKujEXM+Cikp1WjaKqK7WrNelNjnGYMHuss61rTVK3ASN3I8WEkhInjvmhbhqewYYbDO9K9wnvPvJBm6gpnRFEk+PKA6DJMkRPe91TtCmUMJh7R9CQ1cHsH19cWpYx4ZXae7cZhc4c/fE/ONSkE5nFA1Q5lLK5aobJ04sNECViBN7+55vqFYfVNJBwn3v7V71k117SrDU0jrgxkGRGLaMLwwNMnNKdc5gQLelKaiEsCJChA6VsZdf43OUuiuT8dsM2WanPkRXNDZS2t2aFzXfrmBk3CKsVmZbhaO17drLi+uaVdrbja7YoWZGDoHpm7I4cPH3izW+FS4q+6d9w/fGScZj48POBjLJR1zxxnvvzyC3brzWWglvNjd4b6L90FMWbVSotHZdkoF4AneM84DozDxDiInrAPkTh7GR+KmavrF6y3N9zevWLVrnHW4JZERYNdxjK1YdFqTfmidLNUeCldXpfKdXE/zOeE5I/tpipOmDRgMphgiFiIIkEX5omcZvzU0/U9YR6p6pr9444Uy2iSj5IUG8WhoANaa6wWd/mMFAwqQpiLC7wPZCUVd5p7dMrYFGhrgZGzThzSRIieGGeqxrJZrXj9+jVff/0rfvvb3/K3U09/3DPkwJQn1NMIoDL6OmKvEyZDlQyLpJxWCaW9EA5jpo1OOB4anBExiuSVyDmiMNpg1zWpyhz9kePYcRwGjlMkDB0JqLdGtF0LaUrlRJMjN9uKdaPZuJohJPo0s3plWb9yrF+a81iLUrqssYxSIi1nssSF6uqPW3v9seMfJJu2UMdjSvgQmPqJylhqW7Gpa3xOAmuGiAdIsdDkzbmzIRunMEOXWUGlFCrJgowhngdlm7amWa1ofGY/HorFzJMeSRkYzzmf7VDOs05lfmkZZcg8CZLPz4xlYH+ps86BJYvEmEICUkgBHzz9nBiiZUgVc5AJOesy4zSSYuR4eGT2mRBygUsUzkI3zKQssGm7Egjwyds4w7p5+fsTGEz2wufZaHn3lwf2yaGKsn1OAoGmwuxTiDMEWdi3o4dkHFlpdMpF7WGi73r8OEGMOENR0HDkQSrskDwKhTGKpqkKZdrhKumlaK3FISRacXBoaq42G6wTXfzJR3RlqK3F6QqfRqZxZjjNTNPM8ZDZXbUs0mSU80/dnnR0RO8JPpJR6FUD1pC1wk8lCBrI2RBTYBz2MK/RrpKNS80Ym1itM9sN2EozTLIprlYOhyf1j4SoS990QE0OZR253ZbZVIXXCqUTRid2tw2rdkfqXvL2h2/pDo/0j/fYHGh1JM++OJ4D2pKm43MY/klvWhbDApXL+1K5AL3FKy0rqWxyWZzDPNPNI8dp4CrusFaeSxpb1o30rqw2bFrNbtNwu2u5uRba/mazyKZ5pu6BOA2Mjx+5W1WotOIPlQghHE4iPj7HIL27PFO3FQ/HPXMIT/2jy0Jc1KFyaWuKHFYqc7Ay37cMqUuwCjEyTxPjOBZXElGlCiEWhMSw3d5wdfOS9WZHZWU0yZ6VqC6IhFKQkjgO5JhKq+SybyzrCrhUiQuHAf7+TTQHdJpxSaFiBTiIhuhngfeiwJPzdCL4GTtWeD+LwkvMOFvTNA1125Q1mwurWWBcZRUkef9zFFcdEqhS4ZIiOotaS61SIYYkegIpCanQaEdVOTabLbura65ubmnXG3mPY4cK/hMpHFCriNmmIn+3fJwxLWHlIuIURQ5evnZSZEdpU5QJgtZBrZmYGMPM6D1TTPgo5KC73YZgNYTEPGQ0icZmXq5rrtYVOhmU8fiUaK4NzY2h2l1mbhdVnzNkveyDSmHa/8JwaM6CcxMzq7qhqRusspxiTwiRaZ7RztBPI/M0Ypsyv6UFlkwJfIqXIFRYYpVz5zEKg6ayhnZdcXt7KxlHU7HdbUlY3j0e0FphtRg4ppRIIaARWw2DEdWQ84iVwGNt06CUZvKBlFXJtC9HSqVdySdaF0oYmXHaE0mEWePqRjDqake9tVDvcEGBtihbMQ8dOXiqqiIEeRCOj/dstyuam62E1xQJaSaGIJJlSRQZlutw6UNeqr0zFPYEFk3nwFdg0WfmmBDnEd93TPtH/OlIHIYz1bgyFasMU9dzfPfAaCtwhvVVRT8PQq3uR1yGZtXijFCwG2dpjGbynse+x7Y11abl5voWYyoSFY/Hjtl7dpuKytgip2aZ58CpG/jp/p4YA6MPrN2GZrMizZbx5Hn4+MjYT6QQqSvHm9cvcFXN/1t9IJZr0h/3DNsWdEUMcrO1nwlRZv+UuiaGCd/tMVoCgB4ySo+o4LHec9s2/Nnnr9FDj1cRqprDw3uOfc+rNzuu1g21z4XWHWmqjO8PpJQJwwFbHCF0CCQN3ipmJer7L7dQvXnBMF2T5wF/DEzhwDzMhCC92NV6TTrdP6veU0xFA/Kp7Fi+mKQ+XQfaoowBV6FNA6YmrHYcssYfO2x94Gq15tX1tcDzRbm/sjXtquXrr254ebPhi1c3rFdbnBX7n6wgRM/j24jxPWa45+WL17zYbLGr/4b/+M33fPvuI6dxJM9yfbq+5/3DPX/7+9/x+uUrmKZnkWMhxpQslJgS0zTQnfb0+3tc6kFV6GgElo6RcZw4HE8Mw8Q8+yKvF8kJqnrNzc1nfP75b9he3VE5R1WqQLP02BVnR3kyEmSzKsGvoC2lYrhUf1yS6PPe8Cl56cm+kWWMhDlSWUdUEZ0cQ3fk8fGBU7fH+wmtE9fX6zOTfh48ZPjqyz/h13/yp3z99W94eDxx6o7cP7xnv//AMPV0/VFc3mMk+xmtLMZUtNdXGNdAVVOtd7R1y2q1AjwpTVTHjuSDjG7FTPCR/aHjmz98T/IJtEO3a5g6cvRkay5ZtgJqj2r8WQP2kgrI59GkczFko2UZnZvWHt957kPHmGa0yqxeramva9gY9CZje1GI2dw11C8avv7NFxw+dvzwu/fEHz+wtpp/9tU1X718wapu+D/+0+9wW8PtyxXrLyrclcLbsYzXleLHXFxAUtG3BvCMv6gKhF/KDlVCdnHW0daWpmrQucjV5FzwaInw4zjSbtfi/XXm7S+agc8b/oty/qI+b9DomKlcRdYa7SqqBhqfcVajcyJ5j3ZGKj9tS0vr4h1HLhI/SlhDAjOKPqZWBvtJJbioDChkjIESUHKcSWFmGk5y041Fz9L3Oh5PTLOo2YhhvSKrgUqBMZq23aB1jXMTQ3fAuhpXr7mym7O3YFVXZzeLRXRjudiXh/OT4k/+AYvn3POvP72wGd/3xKGXIXTnKJeElEUkeX29YYqBZr9n6HvCmBmyYwwjs59EFs45KqVlNihL0tg4hbUVyjVU65Z6u8bVLWBISZIUrOF6d13grUw3jPTDwMPhIG7ZORG1GIZalUnTyDyP5OhZVxW6Vmzblqt1S9bmfGo5K0LUhCTkmpxHco7EoMv5adDy/vthFI3RDCYEwpxRKTKMHqM0N7s1CplzNCEwDoFhTLR1i9WK6L1I7CFyZtlIH1yp4radAjGVWci8jMVrNBbXWHRtMK3DkAjdSFpQj4VtGJ+z15acO3O5t5z7v5fnJaNBW7K2KNuAXYFryPWaYGtGNA/dSMyKum7Z1U4k4hZoUBnQDqUd1jrcuYI6/2baumHTNoyrmlZ5nDF8dnvLYYpk1/Dh/p5u6DGj5egHptnz3Q8/8Nd/93eY6Isd1LKc1ZnBnQrCEWMg+Il5HlDMYq+kNSHFcxBclKKWPn6MkapqqNsVN7evWK+vaOpWXB5kzJFF81M28LJ95yVJvKA7T5vp4gBxqQA/vR9/9FAK016hmg0RmKgIyRBQVE3LerOjn/agNMY6Xrx4hTWOeRZJsRQzVS2ax/M8ohXUVcX17oq6MvgwM84DwXtCDPhpXLSyMU0NxhA0JD8wRk+eO3IOpDgzdB3Re3RhpqQZxjFx/9Cj+YjBk8LEPGsCDUk3XKIgZyWphQS5tFoWBM2ncF6Xusj6eQPaJmKjMNsKtR/gmMgOqDWmtSKEUlmshlqLbKGwcg0Rg7KapjW8et1QVYqQA6NKmE3L5vUWXSWyElH0nNJ5wxRkcJE3yWQVCyEv/H1372fHL4ZDrbHUrmLTtjjdyKCisZKhkQhk5hIERQxagdVPoAcu8M6yuozQGYzWWGsxWTBlax3ZGLStqBuND1A7YVfFecasRNPRoIiF+WZy6auQcVXxvyp065iF1GONxT3RSJSLmM4w5LJRpXkizANh6hj6AzGLXFFSIyEmjvtDYVZeZIVSjNxdX2Pblna1oa4T8zzzcP8BV7VU9ZamblAoQo40TVOckRfMcxFqWrai5T1+0u9bLmTOT87j5zmP7074oZdZqLYGXWNrW0xUJzZhh0+ezaHh8fFIGCfiZBjzhM8epxyVq2hdTdfJwLTOQpVujGZVVdTbNc12QzdJz4IQsFphlOH26lr6dvPE+/t79scjHx/uZT7UKMxK41XCqUwcOvw0Qgrs1jWNrbjd7bhat0zxqasHhCRuZEpryJ4UPSkI7V2rTFY90zSKG7UxWKVYZWE65qjouhnnDLdXaxkETqJo34+JYYTtXS29xXlmngYZ66kM2VpQGkVFSrNsOlmcOFIsLEc0STdUtaNxFrey+G5k7kaStWQtknCpyLQ9a+OeJ+Pzs7WgyvMi0Kckfsk4MBXKrqBaQ7UiNRuCqUjK8rHrmWKiblqc3WCtptFSHmVlCVkk7pQyMuOngSx98ZQS67YlbVbk3QoVJ5I2fHazZdQNdn3FDz/+yOPhgLEdU5eYvOebP/wBZytcjs9QCVWCFHkBgBIpePEbnUc0EzGLKoiPEuyGccb7WFSghEU6+0C7qmjXW168/Iz1WkYIrF6Y1vn8+y6XMp0TuPzsEbq0diRwSoZ4TtmXFs0nleH5nFCY1S1qtRG5x+Kv6oFms+X65o5T/yh7k0q8fv051jhOp46cNd4HnLN4P3I4PEC2GK3ZbXdc7bYoJX3KGMX0uOt75mlmGEZClnGoMUX8ODKHyCl6cYaJnjROaKCyFdlDmhXDCB8eOvoxYIgYTRFJaEm6fXJhhJQTo7D2xQtUhnx0uUYz4k5vABMzUcGYM9Zmcmuw1yvMxw5lMslkslOYtqJuK5raUwGVVtRa4ayoDoWs0JWl2ThefrZm6jKnfqa3iqurmt2bHdqJjmkI8UzUld6pet7HVZlYkqdfevziIGiMiKJ+/HDPpt1iTc1qvS6QXqSqatomsttdYZVDJcO22QjLLARC8Oesy1rZDFRjJeOLiTlOpCkR+8DjXh68kZ7+NDIME6gK7zXdyVNX5f2gyUlEmZTJWGuloizsTKWLwkHWKJupXU30z8cSlC50/1IxjsPAf/zLv6Q7nRiGXkg8yoCpCFHgmv50Og/5G2tKQ1sgn6br+PjxPUPXMQ0Dh+ORx/t7Pr7/icparLO0qzW7qxvadsXc9/JQnnsQpVFfoJhndPpcZgjTpZGvjWY67nn48Pb8PQBJyawS2qIrR86R04cPhHkg+gnj4ObFNavdlvXVNff3B/7w7Y+IW6bM/BmtaeoWpR0xRoIPZKUka7cNq3pNu96yPz0yjJ5h7CQpSInf/eEt09wxzj2H44k5COVZGyO9wBc1btOI8ELv2VrHanfFdteinSK5zGAHTvP8JFEAW4m/2rj/AIg3ZDa19DxDJNFhlGK3a1GmRgM2jIw5M4bEkDwmZyqlUTGT0AxKMaVESJGbCmwa6LoePwW0MlSuJtcVGEUwnuhHUpjIvljG5CxkITLaDoxeZtT0URVygyEbUVvIKROHE1N/4GkUNFqXIPUclk8FPNHWoa0jugZVrVGmQTdXZLciu5bsVkRtScbSowgpkfZ7pjhx3VR8frVCBYWJho+HAas1V6sWZysaZYg5cjgcGYeBl9s1+voWM7zm+P57fI5YEq9fvmB19xk5Zf7w/Xf87g/f8vj9xDz2DF3HX/7t36D9dO6nwUXkQW5f6bMH0bk05UxTzCQis49S+cyB2UsPcJwjGY2tV3zx1W+4uXvN1e0drlpLkmMjWgm5LRdGtyRI8kxLq6G0GUpP8mz9dO675/M7XFoR+amQ+YKxLvcEuO88E0LKy2lRrlK8ev0VN7ef8fWvfkNKMja23W0AhZ89h8OBcRwZhpHDvuPjxwfmWZiiy7iSkD5M6Q8anBPUSGvxhXRaszaWmDQ+eN69vWc8nTgej0W7WL4nzJJYai2jOT5r5oTAibM4uNyf/CVBUJloPLGwbGOOZ+UvmfcWuySDxqJRRuYjDZGkJeDVm5a6rrHa8HA4UDUKf7OjcpZVbWmtRhtHVjXDFBjGiaEfWFUOt25QK8f7hwMfuxFe7bCvNlR3DbMfMVlhtVsavnJ/oQyMCwdCcR5e+MXHLw+CWqR0csHVlVIYY4X+n4XpVFUVu+0O5yrxZLMNKonay6KWklMuDfRM8pFhGMTRwNW4bDHRsD8MjCGhu8TYS1/A+wBqJqMI4UEaweTifwXOauq6pqqqIvYqQdDnIOK4WUEIjGN4lhX64m4PEEJg8p5D13E4nui6XkYulCbrWaqGmJiLjFPOGVsU1pMx9MMkJps5M/Y9fhoJ0cMs12jSGueczG2lxNR39N2RhblGgWyErRYlUz1nowv9v/zb0sg0WjOfDhwePzzPwI0wz1IozNcU8GNPDDM5BapVg6s19cowTjOmshy6E6exZ5hnQowYY3GlAgqFICRXSpGUIaJIxRtMTDYzIQbmeeZw6AhxJMSp9GMl4agqh20s7WYlPztDmAI5yFiAaQy6gmQDUftnsMZyFUiRME0YJ9BXQujRKhc5LSXIglJaaNNGM4+BYY7S0zUGYy1oCYIxI0EjZ5YWSYqlDstZ+uFaxB/SUvmFTPSyScSc5fvJkBIqz7I5GINRDqMrEfnNgjaYFMnxOVwjSU2p7p8worQSQouxjmQrsqvIrgHboCsJftk2YKoCd2qwlqQVM3AcR3KYWBkhLFij6WdHN0dOo2c3R1CBmBKH/YHueGDnblFoms2O4/17YlT4EMkWnLG8vHvB4+GAq6pStcrzPcwjahr5FJlQTz6XQLhIxF2qtMVBIgap/IIPwghNUNctq/WWq+s7Ntsr2V+sKlDohVX6jOiSL68tP/8s81g2yU8JaBdGaPnmMzj9yZEzc8zYkHlK1NNK4aoW5xqcqwrKlKjqMgPZJJxbMU8Tp65jnkfmeWCarQR/v4waXcbRFKp4eErLRooJuc2pMIP9OODHkTCN5DKTmFLAaM04VgxTT1ZK+oRlVnlBmPyThEVeTWQiQr5a1LmyfGhB3MSxRQJrRoQpMIpk1JnoZOAsSh/RIq1ooXKaqA0RTVLFbi0FXKVxxpCzZgqBPgTs9Qa7cpjGnIsDQ3kvZ7j70mZQeSHx5J/fs//M8cvhUOeEzZgCdV3jbEVKiKzZLKapq9WGum6ZphmForEbCOPZeiPGwOxnDl3POHsO48TDwwND33O7WfPq9o43L17zh+/e8+HxyGGKhDmgFLy4u6auhX348HAvQrpaU9eiZFNXjs1mTdu2F11TDVkJ06kyjsrOjMMlU80ZTqeO/eEkCvQxMgwTfUgcJ8+xn84ZxXmtlKuriiJLUg6UJqJ5OI4YU25mFL8t5cQz0c8THhimgW7o2H98h84y8Shi6pm0DAdmiDlyYQuW9wBPehoF/lEaYmAee+KTStC0MqsT54EweaKfmIej6KhWmmbdSq/QWuqN466/ot5qvv/xIx8/Hum6idbVVK5CFMMSoxYPwJAhKcM0e47HE2Ee0Tmyqi2H/QMP+wPf/OEtzhmq2soMWckkN+uWdluze3nLHD3zNHN86Mo8ZsKZgG0UWIGScvbnBS8XIZD8jO9n1FZo2in1IopgDFqvZGPUMpeostC3+1PPYzdzd7Vl3dS4uiYgULlnsatK4nKRDESNdYmcAz7MMGWy0gStUEmjkmYYZmIOBBVRiJjzHCf8OJBDYNM0mLrBtrBTCp0y9BPaKQzPg2CIoczQPWXDIn0767DtimAqJleT6w24Fbreoao12BpsI+tDKbACrylruN+/53EemI8fGV68JGXYtBXVEPhw8tTtRDtHCBM/fvctD+/fsspfsdu07G5f89Pbd4zDzOE0Ms4nkku8evGSH9+/JyOO3klptJMe18/YoWdizxNAIwlkmssIU07Cmo1FYWie4nnEJisRQ//yqz/h9ZuvqZuVcAKsuFAYpRC5uXwOaPrJ9TtXgXAOgAvEuVQRMoyfL9/0lGS2bBSfBPYpgIlL8JP5Q41YUsmc4kp+o7oEa200N1eXTSTnSM6RlGWsYZ5nxnFknmdOpyPDKI4Zh/2BaZro+17698ET/CiVUM5FRGCGGARmB8KsICeUzrx//yNVvcFWLa5qsc6KzJpdZmyfnHr05ChjGEZB1kWfWBfJxiWZA4KTa2XnhDaW7DRJC0HpMlhf2hcWbJVZN5ajMUzKkmsnPojKs7YtjbbESTOOArGvbxuaqxq7MqIWJqEQ8sU7NaX4rAetlSrM+L+f1PTp8ctdJErlI9WWwyjD0A+lYjqx217Rto7NeoWiZxwnfv/7b0sV5+n7jr5oUe67iXFOHEepBHOMbP70BlSLa9aMPtOPkX7KxcYjM84zyli01Zw6fxZotsahtcEYh7WTQAhPGHbWCaxXO4vVAkmkuAhZZf729z/y4ZTPor0hBsZBLECYO2IOXLLJy9DtIlZnlCqzX+B1mfUxVdkeMkoXixalsMZitaGtappaoMGYwBcZojFO55urtEANT+cDU1qorYJ9y6HIRqNsfNY7c1Vh/FUZ49qS3WVSCCKZFl0JTAL9WA3X2wbiNbtVzfv7UWCqDH7youBBEg0/FIduoj+dJNBoRYwzw3jk8bCn70eMUThncJWoCwEQIrvW4WrDoT8xzL1Yr0yTCDwoTf84Y4Ki2ogXX/CXOiID2WRUbal2G1KeCXMoy1g2qsqA9zPj1OFT0UOl5od3H/iw73BqJM6WNFls5Rh85O1Dx8f7AzEk3t+vqG2L0zXKD8LOTGCclIjzLP6TioSpNSpZciybHVkIXdmB1azqCusqjK6JURHIZGOk5/Nprrr0pbKsS600bdVwe3PHbndFUo4haw5ZcVINydRoV1M1LaZqsbY+w+ixKHy0dY2femKKfPj4Tgxzh6H4tnlyFNuZXWtpYsf9j7/n7e//E+HD73nx+jWff/0rWL/A1eB7xTRHgh+oqwZjHKvNThKpMBdd1ETWhqfCaQuar8snIjcp1W6KGR8jWUM0prQbUlGFCaScuXv1gtevP+eLL7+mqldi3Fxg1LMf/Lm/V7SazlUgRcv05z1zWVCXZFjAlScB+9k5PGlJnL83iVSfkt8rX1023gKRKiRAqyKYUVCN571Gg1ZrnMnYJtNUUvVd7WZinIhRRoZCQazGcSjuO8VrNfgnxuMzsx/OaIzMTxt+evsRpfeCFJRKzTqDtYqxPz3hHGREvs+Tknpy3hLshACozzdVKXnuNIXZbBXtVcXmpmb3ouH2zTXXN2tMo0g16EbTrisOaWLqPMZt2V5ZPv9ix00ybK1mOowMR2mB1cmT8aBkLWiWih7Zt3KpCs/VIGWE4+m9+D8//gFzgpK1nYMMihDkYo/DxDTPZzhymj1qEv8v70UxputO9P3Afn/k2CfGObHvhDwi7ZAaY2uMdfiYmXxiDnLSBvG6q7Ms+hDFhDeGMnCqS5ZHkAtQbpxS0DaGymlSU2MU5+C53PP7fcesjoVGXQZZg2SnKkXp/eRlfq+QGZYgqLT0bPKSoyjQimh88SATeCAVmrhWCbTBKoN1NcYaTFKkCJpiOFhmc3XJZ5esdhEKf4r4yCksxf/zh9RojTYWbR2qtmSVqDZbwjQRZ0+K5Roh85pkqK1lsxJiyDiLascwzfgYmUNgKvZKWWn8NDGMI+Mw0LQ1IU503ZFpGonFIUKbAuUZg0HU+5vKoAz008A4Dfh5RqUImFK9ZZSHHIvRus/P9zCtUNZg6po4y4aho1zjBS4OQQhane8LoSfwcX/k48OR243Gj4ap0jRtQz9H3r8/cuwGAB4PJ1YNtDUYNQmsmc1ZKipGUCqCikUqTAsTr9w8pcR+h6zLaELGpMjki2pK8er7ua/lZWNUWdZO7WquNjvubu6Yo8aGTPCJMVmCtmhjRUu1rnGmknUaIwGFsZbGVpiqETNeHzl2HTlnbq52qJyotOZ6t4JYodTI3B+ZDve8ffyJ4Cdcu2H74g2mqtAhwBSKcpRA5W27wroK62diKJuUXohnnwaep8HlMhaUY4RcpLByPpOGYunzbHdX7K6u2e2uAVvMiJdVX/qsCzBS2gaKhYnKs/dx/tsS/Jb/ZT5BVy73QXGBJZ8eT5866XuWWcgCoGZysXl78qzmVFpZ8mws71VhRQLsCTmnEc49OV9GqWKMzPNEjAE/96K76T1TYdPOfmaaOnyQYCmKO4mYFCkGUvZEJHAvjNp5Gi5vcDnpJYN4xqR/Mo+sntzjJSghJbhrDPWmot3VbK9WtJsK5RCncaewlYEwEkJC2Uiz1tzerVmfAk7B2I3Mkwh2CDRb4NmioJQTRRLtMiIm1zTLXfjknv+S4x9kpbQs4XmeIQeMtmgMMWbe/fSBGDJ3d69oWph85P3jg+DcMRC8VAw3L2/ZRkM3RPpv74lZJMS++OoN1zcbsoUpiHfWFDRay8kuThKurlitNyhl6U4jxtbih4YY86aUMFqaytYobl/uuNqu+OLNK5yBcRz53/79fxTJMGBOhjEsOHm5uaYh2xW6mgkpivQSyFiGXIzzNTkH3dJ4B0jJX+DTJ9KXlTXUVY0pDD/jFDFlxnFiGkfiNJVFdXnglnsqBeAlU81lzinnjE+BeRouwR1QMaOT9HK1sYJlrLcYVxGniflwYB68DPI6UYbwkyZHyaevGoPNiTQrPkwTx27gw+OBzW6LdY7udE83DJz6kbxXkCOZSTRAV46soqh7+Mw2R65XDW+2K0abOHnP4/CITRqXNdYpbGuoNpb16xZdKTIzQz8x9hcZLgUY26BszRxGaK8EnkmZtAxUxxk/eqYu8nc/PHDoJ4YZ7vc9/TBxuD8W+5yMrQ0xKbpeoU2mcuLzt7vq2F2tWTUGZyyN04xzJ5WMXrOIwmot2TFo5nkQGawwi2pGTpzG+3NPryOJAajRaB95+Gn/7H5pa2QEo7AZrdJcrbd8dveKN599Tj8m9pNHDzN+DEzKgrGsm5bVak1jq6K0HkklQVNKo7c3pKZhlWcO+3t+fPcTMUZeXF8zjGIg3O1a6mvFdlWTr1f8zX/4txwOj/z02PHf/z//ZzZ3t7zeNaxPPeM4M4wTbVXz+sUrXt28wCjLw/5BTHL18y1FUJRSuSXIMeKngdlPTH5GR48ygsTI/fNCMlIaV6/4+k/+lBcvXlE5SY7P1wsZ2H4KX0onajmeSJ0peUaXUSh5Ri+tB1VeX5L7S4h8AsM8DRQoKmNxxiwxDzHnViLHphJgLlWwuvy8ZYD//IjnjPjKCMx4xneUJMeikCX9RuMUzrYIEnRVKjhJGnJaRAhEj9TPswiQhygGt9ETwswwdoQgA/2znzgd93xf9i+llOzp2hS4+rLtLwxamddMoEXNSRj9TpKWmHGNZX274jYmtrc1bmUJLhE3mjhokpP3iPbkemDdOm53d0x/+4FwGPn+7YHj4PHaoiuFMgmyxylLTlmEBbT0KUWExUo4zvHJXrwY5P2y45dXglnguDlGkgaVDcaKhUfbtoToGceerjuKpFIK3L24E+puigzjiELgwm6YmFIAPWOdCDSP45G+z2gV2GxabkOi9grvO2KUmzaNPWPluL7aUFfuzKhUOuFqOXGtNZvNhqauWK0aXty2bNY1L26vMDrTdfY8F7QsNmGHyuLOKFAO02xQ2qHbzZMH4JOqZLkw5x/Gk2wunxOq5UtaK5IxjKYmpArlZY4xqJbgLOh4zqyWI56b/CK9RoHLFvba8ndU9eTJUtIfswZjM0orculJYC3khCnN+hgjOYlrgXE1JiVszjgbaWpFzoZbL0IH9/sD0zgye6nejQFjMt4L1Coq88K2rStFi8IozRfXV7y42vDFy1t6PI/zwLu3PXGSB6duNKsry+alQ63EVLdpbpm3gcoMKHUx1c1JFV+zVKDDpTouwsQadKVpmpbb6yualWf2muvdBu8D9RmxEup+RhFSgek0NJUwbmcfcCiUSwQVSlmgiEYEvDURgmxU04xkqlHhx0SYZ1IM6DwL5TwkjtNUyA0RpxUPh/75vsqS3SZh4CktpqtNQ12LU0uTYRUSmwQWQ1DQKKiVotGmeD3aMzUuJyRJdIb2xWdiLpszXd+hkNnbtnZ4v+Gm2lDVK25evuKzN29Q7RXu5gW2atDW0jiHUmuapsYcDVOITCHwxas3WOuYZk/wA+lntLx8+VAKSMTzJjxjckAjyVoIMps5TpNoYlY16/XmDKcv7D9VhAmWMkugzCdBiyX4Pn9teTdLL+159VcCz7kQuqjKKPXJz1Hgqpa6XpXPdUF9LqpVShuBSUsVKbJ3F7j0wk59InrBgrjxR977E4UbCvJVEKrlf7JNKFTWaGWxRqGVReuES46UKpxz0tcPoipjtTvX7TnLDHNMCpQgfhmk+i+BXGfpuYHBKCt6vTGdSUY+J6KBXEG2mWQi3iioFanRBBtp6gqammrjRE6zUtjrCqUyMSoa48hW0W5btNXM84yZM2QpGooBrFwFtVwNe66yY8zET3xI/3PHL68Ei0dgLCoZWhu0cVR1xWrVcjwdmL1AYjHLJvXi5YvzLN2p72T+BvA8YGewLmBMprKZcToyDBmjE9vtGoxlFRSnU5JGaX9ingamwfHq9RvapiF4mTdSKrPeOKEPO8eLuzs2mzVXV1uuNoamtmw3LVol6up5lS+i1vpZ8y1rh6kttt7gnjTJnzp+n1+LS5+Q8w/WSl0W9rlfsSxTmFBMhXglsddCVSAVlly0sNaWTDrnc7CWwBfPD3HKmaxEkWI5lClEEZvLkLeoKwieJP0wUkbPQXj4WWOrpqjHZ6z1tAqchZJj8f1PhsnPxDnhaiuK/AbmORW2cHUeym8qaI2ldY4vb695dXfDl5+/YkieD92R3z285zRP+BSoG8dm57h9VTMmj600t9fXpJRxqn9eGedFkDxRFLJJyqAQe6qkxfKqaWpe3F6LJ1kyQldPER20eDtmcZ9QalEXEV3OfjqKJmaU2VdDItl0FmCIi4uFSgLZ+sw8QuUMKotH29CLWazWiXkKjINnf+wIfib7iWbtOI7Ts01N7uVCkZeg3NYNdV1R1xUhRZqUWfnAOitMVowa6hIEa20EftYKYyUxiD7idI1RFbq16CwT1z+8/ZYQPZlA2zSkFHizrXjjWrZ3L/nsiy/J9Ra9+6yoJFm0M2UDRTajLH3wLz/7DKUN9/sjpy4S9PMM/GmwWTbuGASB8H4mI2bKaHPue03zTOtWOFeLLmhxmFjWwVKxkS/BbpFjfPp7L5q6z8HMpdLL5yxVnTfV5ZldKthP5QgpP825Gls1JXjqMvplSgWuzkIYAFow3LMLzPJTFvao6B/nc8//+eiGBM/lMl7eZj5/zzkhPqtPAchaUCqjdQYsOReeQDm3GOMZ1ViuSoxZ+oG69P/KXp7KZmW0+CzqbNDKSSsoyVVOGXyORJ3JtSLZSDQlAa80qTaEKlGvauptRbUSL0tcliCoFS4Y2jrK8PymRRtpuVWz7FFp2byf5VoyHifXR4kA/C8fE/yHVoKy7yhTSv404yrNbrfm6mYlJrZpwNkG5xx1teL33/6B79++JZCFldTUoBVXVxv+9X/3F9TWUVvHdt2yales2jW//nqmHyZGH5jDmnme+On7tzRVzXq15i9++yXGWMbhK2KU4c1V66THZgyr1bq4QZvzcGilRenDZsun/bMlm1gyi8vCzxhlWAKSLkXZIimkgGSWIFjSVNQ5CEr8W6Capwr1i1Yi58W1vC7/lZ4C0iO5UEOewD/n7r+sh2fzhILtgFWIVloJ3klmg4L350FkAfJdYX8ZXJRehloZmQ2Mgapp2W3WNI3j2x/e8vFxz8f9kWEKTHOWTNgYKlvROkNtNddtzYubK+6ur3jz2WtWbcN63fLaOV7OE3PO/NW33/H24SOra2ivFM1Wk6aEMRGFOI7PU39Zg0DWEW0UdbUiTzKH5Z0izYrkRfZLZA61wOZGUyvNNAzMsyfqVWFcVugk82oxBWKeCTmg/Rpra5xpoJ+QClcSiayUlL9JnABCimQla3q1bUX5o91xlQOZRG1hmiPDlDgeJ8Zxpns84mqDOh1Q6v97Obew9IRlE8pJyE3rzYarq2sqN1NXFdYYmDx9gL1XrLSi1bCuzHn9q+KGkLQR8QKtaGzDXdvy28/e8L/lmcfDAx8+vMUoOB0fqONEt1N8tlLcvvlzmt0dq7sv2F6/QFdV2SvFmv3u7pbNbsvt3S2mbXn94iXGGL759u+49xPdkz7Yct+Wv+UsI0Z+GgnzBEr6fyoKoiACC56buxW73TV13QrzMF+kzS5PwiUQpCRPzB/fu5aKTp0/T89URy4IS0pi/xXjhXV4CaZypJy4f7hn8qEEPKkArZW5Pq1lfEwvRDllLwzSs5PMApeKpNvyHsMyIpGeBvRU9qbLHrIYgGu9jAPJzw4hnM9v6SWGEIr0nOwDImDycyGAnDOHbkA1yFoq1eo8z+frZa2jspVYVZlBRpOSqFClmKFSqDtFc92QG0g6onwSU+qdovlqjdu12E2DqwxRZ3yVaH+9wZ480TzQTlGMiqvAnCKhS9xUsjfH7J+gQaVfWvYpY4w8I86WYflfVg3+A4gxS0K3UJqTVBAIzGZsCQpF50+hismtsCTbpsJVFc2qhVxL499WVFaGP43SOOswCtq6/Jkrsl4RY6TRmso6mrrhZrvGWEtY1QgZQUSqjSmLzjmBXhUlY5EBTxRYbf/Io/LJK0pxES8rXz/HuEWhQB6ahUH1fEEtr+VSeS2vlgf4SQOcpM4P4flr6pKRKn2BR5frLw/EGbcp3/ecWr8Y8yqjnmwWRUw7hrP+Hgj8xyI1pS/9JKUl8DsFuXbcXG0ZhgGthF1obEC7KHujMbRVxbataSvL7brherflertm3dbUdVVMlC0NcLe94nbzyOgHttew3lqqytJ3mjAlTr4nZiECPN1RYxS4RmUN1okijk1S3SGmuhJMlAyYG02lNKlKoCxRSbBwTkOw5KQgRnJU2KRZuQpjxC09NTVCM09CelJK1IxKlq6zR1RrHVVjy+anZAQgRVIYcZUGq6jaBu8D/arG50yfn64vSlUrSvhaiZxUVTc0zYq2XaEwpfcNAyPoyBBls7Ck4pwu3mu5ECZj1v9/9v7s17ZtS/ODfr0axZxzlbs+5a0iMu6NiCzCYTIxwk5DKi0jhMQLIAyP/AFICBn5BV4REka8ISQkeLSFLCHbUmJ4sBOHM50ZERn1jXPPuafc9SpnMYpe8dD6GHOufc49dx9HZtiWcujsvdeZa6655hxj9N5a+9rXvg+rMkaJSkdTtdi25sHZGYrAMG7IcWQYdtys17zGooPFna2AisbUaOsw1gnzNReFDgwVoLTm/ukJOcPNdgtpoEmep4fthmnzZh9QYpT+bUqhwGxAFgeQGAK5SCe27UIEFkr2P+d4byADXwM9D9bnN+2D0xo6MOaU9cFek3dKV/cvcHCtMozjgB36OfgopQlBY0zRMi5+nlorgQ2V7HFvBkFgDnpT4DoU974Lix68H5XmIHpIqppg1umNToFzeu702Px880blHrPsDUrmbUEcIaaqm0x5n2F+R4pYtFjzbDlmopaRoyIm4KxBN4r6fIFdVpjWknUm6UTMkVwZaBR5oQoTW4HNYielNHkipSaZg5R8McyVsdZyzq1zGKPlPd+5fr/4eOsgmMqgeM6UDENBki3AaFVwY7mNhA0S6IYIJI5WDSfnZ9RNw2K5wGqw2tBUNbrU+ULDFxiptZra1VRNRd22GGvJ771bVPDFhVyVhVHVoosZ4jh/4BDF5oeUsUqLtQdyYmpz1z/rbtA5OGWHgWoKNLrg+4VJOqEvUnjts0zYO9XvL0OeK0UUHLpZTJZE84uVH3rzBj+EaaZ+yORb/qY5xtSLLajRPFgbRk8cPDmEmRUq1YMmFpX4ycSYrIvbfaKylnunx1TWcP/sGDRsRy9DrRqs1jTGcX5yxLKtOVs2VNbhrBi22gKjgNhqPTo65eZsjbKJxXsZtwDbZK62A7uN57K7xJ0out7fuY3HEfyIzCFVrVR6DGSTSXVGRw0poxIYbeeFbGsLDip6mR81mZD1nCAQFDppjquKTNEeXC7kckXZnESQvSUYLfNVoSu2MZZ6YdBWo6lgiMQx8Pr1Ncpamqbm9P4KrRRhOOP5Rc86TKwDudbZB7mftMbUBlfXrI6OOTo65ujohMb2tPVIUzm80uhuYDt0OCKGQGtkozHOFAspikiEGLlWWnHc1Cwbx/fefZdFYyB3xDASxx2XV9fkvmLTViTb8qDJ2ACnxmKdK/dUKmhQwhlNbQ1P7p+zWi5ZrFY8PDvis1XL7/57mjh5vk09MTWD/IRxIHpPDEGStJJUD8VDMKVM2y5EeMO62fVEKkHmtVpO3WyDNleKU7WYp0CY58fmkKLUXP1N72tS/0n57sq906QvrzKOPaZUYPtgpOfAp9Rk66QoqsjyvQO4dBLBODzuBvi7fcDD4HYYBGOMRT3H3tkzJNDm8hxdZqvVHHCNEYj78Jj8aHXKOFX0oY0T9Gi2povC7lSmiA6I4k9WGVtrcoQUMt0YUGgq7agri600K6fINpNNZjSBSCQSiNZAnUlHBpOkms4WtFNU1om4CgqdLMaKXZvWEIL861yLtY66biBnnN3ytsfb9wSFjwVoQvDklLGqKia2ZrbemHo2qIR1mqPjBU3rcFWNcwabMypEMoEw49mJFKJAfxlOjhYobTCVnZRxUU7jjMVqS0xB+pMxQnaix6jSQcBIZWBaY7NDZVE2ycXH625ysM+01EFyqBTFtPfweeU5ezBmH7QOXkuMgpm/p6b+hZyh+bnTglaFnCpN8UNYdvp9008cvNfpq4PewOFnSqMn9SNJTVBFwG93JB/IPhaVlzIeIZdXrrEVQs1EvJNNRfpnOcLRcsly0WKqivVuy+1uQ/aJqnIcH604O1vQNo6mMkV2JVEZgTitTeJGreF40XCyWrILA9urHeOtEFvczRFN5xm3HdhMHu9mcs5ZsWNKkWbZYK0h9VHGD3JC95pEJOVI2NxCzhjXYOslVe1IIYkQtQGrHSFBFyKuqiRIjb1UBEpTmUrOcxho2iVaW6xy4m8XRzQBU9XUzZJmsZKxlOgJZZMy9VCg0iXOSULhiDy4f8p20PsNT8HxYklUmq33uLqV+b9CNNJKcXpyLBR5v5xVe15d3TLu1kTvOW4XOLUQRQ5liBTr6ZzQZJZtjXVyg98/P2W1sDw+X3J7+ZowjqhsqYwlKseoakYsPiZ2ux05J9q2YbJEikEy8KwUlda0znDUVPzog/do0oBWkyVtUQ0p1U1IEe97EXbwQh7K2olgQcwMfU8/eoahwG9KSSJXbvtJDa3kf0VfM81rwOi9/92kWZymnr7aw4l31kp5nZQzcfYWnCDQfSCKbwiezyoqilmv1EwKNsUfkClAlg8gD03JcLHGQgkawfw23/j3LsI0scLnygyZqzzc1Kb3PPsnlqA39R9TkZdLKbPdbvd7ioKmbWjbVhCFg6LAWIvOiWHsy9iTwjlRfPIhkhCGvkeKoWShUpWYFmhLMhDIJCMsXZF0LO5B2UmCRcYvLEpZMe8NXoyd08hOC/qjsoi2OGeoG0ttHUo5yLp8np3oO2+7O+fk2463tlJ6+eoakBJUgmCSpruxQsPXIOa0sQQA2TxDUTHQeitGi1Y2R0W5aZPcqDmlwmpWOOdQRqOtFXgCyeydNlgjeqOx4NDGShDMOs/ZF3kvkeRwkNR8E6+33R2Fge72gjcVIjJTZkeBgfMMfaqDxTQtlDkDLTdgSmn++cNMb+4JHrzOZO8CCNmlZJSHz51eZVrYhzXm9Ll8v50r1pwzP/34c/q+l4AQRefUd4PMZRWCU46ZFDLVaoGymqgivh8Iw8i4lZELlWXjSVl0U62VhGc3jGx2PZtdT/IR5wJDNgwk6toil1mCr8FitC2bukPcIBJfXtzwar3lBlmIOmfSVtzq17sBNSR2YdzrqubMF0+fMwwjMQWWx2uMMYR+W66F9JtSEh3RsLuFlDBVQ9WuMK4ihS1ZG7KpyKMlhMCuv6GuarTS+H5LVIqsDHW7k8a/H7BNW1wpDL3vGcKIUgNVVbNoj6nbpfSA4kAICu8T19c3EgSXC6wFRULFxJArXry6vJPlqxRlUw3iNB6Gnpur17x8tsBEz7JphcwQAlcXl9zebthdvsJjUNpRxYGuaWmblqQMKSPkgOgxOsOmwVmpyPtxTQwjKXTEzQ1+GAg+02mDMRarIv1uTb+94eriBU1Ts2jbOcEdRi+zcUqBtgwxcrPtMQZuXr+cUY2UEp99+nNub25ISDDstmuePXvOzcUl3XYtcnA542Nmu+sZfaDrAxevX1M3C5bLY6mect6jHYp5DnBaEyozk5cUzH6FUzCd1tIUHPaJJbNiTUppdro4DDRKKV4+f3Znfd3eXDAO3X4tazX3A6eqcIpfgkWpuQqc5duY9oy7ge5uIHwT+i1BfxoByeILmguhbXrinSCYMxycjwlNSjmzvr2eXz9FeP75lt1tRBtdiozpjZRRtaEvsL8Rkg3gfdjDoaWizmSULjCwlhihgJxKfzInspkxMlIQ4qXvvVigKS1+lVPCWYIgeer7GRaLWlpgSpEShJAZx4CPieef3b5tDER9E/tp/qaaZUlKZvMLnzldprf5lQfP+0WY7S/8RW+J8n77O7uTCc435D+9YwpR/0V+jm/52W//3HlepCCWTt/Eifum1/zGZ73VSc7f8J7fuE/m/ss3/Xj+5l9TYKqpGj68RbXeby7fet+9+dB/kZP65q36TT/ztfdy8KT55L75PtW8eX3bL7+TSL1xTqdN+u77+PZ18/Ujf/16z79KHZxn7kC333wcbGgHVdNh72t+1hsw39feU76bKP5lHd96y79RQe7f11/e+/und9z9pHfmVc1/HT/P1w+J+3eq418cvd42CP7z458f//z458c/P/758V/H49uC4Fv3BE+OVhjzTf4U31Yh/qJ3dPfH/zKPnBLXt5s5+zk5ORHrpf+aHyklbm72KiSr1arMKu0zekFSD6q3Uj0c9qa+saL7thL1G67l12qinO8+9eCLqTKYnvcmDJxSKn0LOeR66UOuwy94Y9/wJvPdrFfUL2TmUk2sPVNYZeoNfOCX3qff7UaOMXJzczP//+Gg9ddwkuk9l/7y4TmaLHaapuHwqfL9u78zlwo7+FCIBXpGDH7hklR3X3P/2t/8ed/8XPXRohBCFE7ZGdafXFMmZ/A9Y3qC8EqbQUsrQ3rYXnpv5fpPpKa5DfLmh52+LH8JoS8zzVErpWSg/BvQhdnKTAv8n1JiU6T1AJarFepNNtrXztG3fvdbf/YvdrxF7VKeElNkd7C+zHQuDqvwOyjBwabwix6/8+2vv5dvwEx+6XO++flv4IIHX6Yszi5vc7xVJWiM5t//v//b/Mav/rBo+lEW0WReW4ZX501V3pFS8QDO0uVNqiLtXn6k4OkUYs1dnOcbNuIpoJcLVVB9iqgc80iCytyRTiqf88XrS/6V/+H/gpvbDdZa/t7f+3v8+Mc/vvO5fxmcKRNJ/9WCDZ4+fcrf/tt/m/V6jbWWf/v/+H/mgw8+xNoKa2Q2sutE7SUEDwasszR1Q7tosVY88EwZunaunnsYKaU7PVGQ3oT0FQ6stcqGrErPYNKH7HbdAUFCLkUMiRiFHBF8zzD0opLfd2itWC5aUkq8evWSf/Pf/LcYhoG6rvkP/sP/Fx9++CE5pbKB6qKKYeYB5f0tHZBBYy9EoRDw2w6ijPl88cVnrG8uuXnxOWf3z1mdnPLgez/G1a1ou5o9w2++1w92tj1SeSAsrOVcH/aIp38PSRmffvop//q//q/T9z1aa/7Kr/0ay9WKxWJZZt7EyDpGT0xiHD3NhA2jCIPXVc0777zDe+++y//s3/ifY4xjDIlhGDFGs1i0+5FSlRi9p+sH/vE/+k9pnOG9Jw/58MMfsliu8CRUyqiU0VYf9NEkaHX9cED7N/Mc7sRILAuSjz/+mL/zd/4OwzDgFjX/xv/1f83jJ+/wsD7hb977CSsanj17ys31hn4IvPfBhxwvW06WNc5pYghs1mvyOKDJHD+4x2c/+5if/fGf8Okf/H2WS82PfuNdtrstwzCyW29YHJ3Trk4wiNxXyGJVRip0/5zw48if/vEnfHUR+OxVJmvFyZHjJz844sP3H3B6vCSEDFnEFK5ePKOuW+4/eY+j4xO+enHB//Lf+j8QYqRdLPjf/u//T5yene/3sIO1Mff8yr1x5//Lc2Y3++kkH95Tbzz2jccbrYRc9s38te8fMGnnzXhKNjOff/pz/nf/m/+V6MFqza99+A5tW9G0NcvVCq0N3u/X/7JtIQuhzvcDGkXjKpwpsHcMJbmMRc5NnjslOToxb+ui11wG8Q/R5SxxwEyCAwdZfEp5ngnV6H3vdhYUEDu3q92Ov//JZwfn559CJXh+esyD+2eF9CHvVpcZDiHBmCkckYu7dA7DvElqbRFKrWXuVimKeP4UBBFPqDnzPQyCU4A8qFqUAjW34ctzpgWZix+WvCtVTn6KaX8DKsW9e/d49OjRnUY4KRF8T/ADu90tKUZizDTNElfVtEdHxUppUo9Xv/Se/WdxHCYw3vs7i/Do6Jizs3NhehlRk9nuOoZBNAMxyMxe07BYLLDWitB1+VPXzVxlTOy+w2w75TgThvLBBj39zOTKsQ+CkRim5rwMBcfghSGYRNdwHAa6bkeMou7S9x3moCJTSvHgwX0eP35UjJpFc3G5OLoTBOXcAGVkJ+cohKAQif0oG0TKxJxYH7Usnefh48ccnZ1z/sGHIh9nK1mEX9vkDjesfbqUJxUTZe5m0QfX6bDS3e3uKuG0C5kHrKq9/F2KGW3AyUDI/NzKOVAKZx3vvPMOP/zRj7h//x4xwnY3UDlhYh8dLyGXClMjDg0h0O9+gxgGnM6sx4FNSgwpctIuWLUN5yfHkswgQ9M5Z/phnKvlFBPGaqyxNE11cO4119dXB+cHqoXDLS1YTVoeEXNLMK/JtkZHg7YtplrgmgVWBQnEPqCGHqfh/OSEzdkZl+enXCxbFgs4XtboPFLrRJWWLI9XLI6PsdqIv2MMhWgnf4ZhYEdm1wW6fmQcJQgOQ2bX+TLHVrFoLTlr+bFNS7NYcv/smMVqxXq72289SnF6esrZ+TlAIcPIJ542+8N75s2AOL3G4X09nbG7+8i31Ez5zUcOuOMH99sUBOfH8yTLJizTm8uLO7/FlHEnazSVs0J8NHsXIZnLVhir0ZXDaMOibqidkznVolKjchYCWxC7tFSSEp0KKWlGcgrJ5+BNTKQh/camOu0d4ngvRdjh9yCXER5N5/2bdeIvPL6DYsw0n0bJ/KdRgCTK8VmicsyJ4AfRB9xtZCA2J/GxshVVtcBZxzy4WWAPgT8UOs1h5SClL0GtKMZPAVQGWuQZaaoKcyqZedETzLkYrk7F4ddPizycybHcJONIt75kfXvBixefMPQD4xC5d/89VifnPKm+j6lqjHFSEx60Tv+rUiGmophgrcU5Udyw1hCCRkd1Z6OegtUEr0lQmwwy9xqHU1WwZ8buq8NpWNUYU+YpYaoOrDOilqESIRSdUZ3F1kWJykarGtRRpttu6XZbXr94we31FVevL+4Ee1mUIrG12WzIKbNaHt2tUst7zVr0ZJVy4jfrMq5hfm/LmyuxZso77r/7Difn92jOz1HawiyW/vWNR3QKvg72fJObxzeRO5RSWHu49BRt01BXFYosc7GFiWesFlaqElhwMoBWSIX+5MkTfuVHPyLGwGbTcX29wVhDXVdUtcaaGrEa01SVpalrfvKT3+Di9Qs+/vjP+MMvn7IeBnrv+eF77/LBo0ccLeo5s7e2QWuFs1bWSUoMww7ty/XWGWsdyrmSs96FIX23Ybu+IqaeT+yaJgWeP3uNX3fkMYG7YL3p2e1GTFgTdmuuv/wCvbuhcYb7H35ATlFE3pWMTAz9gO864ugxVGiEdaztJIWnSCEwqT2EzY7NuuPTp9e8uh642Mj87LBz1Grk9PgEa2vu3T8mF8KSUjLy4Iwmp0CM/s41NBqsmmDpKajlfdJUHtfzc8p+p6Y9YhqZmO6A/c8fwl9vAmFv/t/d+6/ck2VDnesJNVWD8vp7MQB4kweTgrDuY7Ti0KKzmPQGmTMe/VCCncEqgdO1s7hWAuFq2eCsxRrNZr1h6Hs2mw1jP5BiREcpMvZuD6pUfPtZaUkWDhPag/31QMd1Ou/TGZR1L/C4/cbW3Tcf32FOcKLupz0spCPlrJKjp+t2vH79nNurV/S7Df3tBrQYO7p2iXUtdXPM8eqItm05PTvFVQ26bNLlDuIX0XEycoGVlmozRM/Q9bMjvM6FZh8Cxjls29I0i1nxoHyMb/hkcjPE1DF0Gz77kz/g1dOf8+rF56zXr/DDwNANJNWyODrnN/7mv8yHP/o1Hr37IegSCP8rEvxAbprNZs16vZZxkxKkUopz1mUKlHVY/U2V3ASBTq81LVgQ6vmkGKGNyKVNLMaU40QGZ0pcyOLniJFEQ26dXHo7BrTCWk2Ogeg93XrDZn3L7cUV2+2Goev3l04pjK4Yh8BXX33F7/zO7+C953/yP/6firpIeS/X19c8ffpUhJ/blg/ef38etjW61G8506xWApX2R5iqAVuhjAVluStOOEW/KazvH7573ctrvwUscOc5ClCaqq5YrZbc3NwQvGfRVBgn8HROzJJYAJWrODs95/GjR9y/d5/b2zV978tG1RPjgHOSfBpjUWNxFNEGpS0xQT8G/n9/+lM+e31BP3h+8uFrfvX99zhfnXJ2tGS1aOZ7RGuRN4s5oc3RjLCMXn6nHkdQ0O02hzci6WYrc4f9wO+/+JiwTTz7x38fdj06wrs//HVWR8ccH624ffEJjFuO8sjDheV02fDpH/4u3fYatXnJQnUoH7l9ccXQdeSsaJbnaNWidIUyGZVk+F4pmVsjB27XHc9f3fD8oudmO9L1QSy3ugE19pwsa/wwUC2aIjqdmDwjM4nddstmvb4TcVQMxQJMJqhR00wgUCTJVFZ7NGve3+SCFxBLfm6+udSdfWqfSB7cg1/bG+cy7+uP5bvfz5miX5qZlcLfmH8UIRC517qux7pIXbeEGBmLVrPRGmsMlatIZFx0MIqubqMa2rZhtVzSLJd47znueja3t4zDSOgGkg+kIKN2M1I3pZCHtc/BWjmc05xUbu4sszxVOcDcXnu74zsEwf0bOowkuZS0Q9ezubni9fMvWV+9Yuh2jJseTEKZjGlanFtQNTtCt2HRLtA5sjw+oW6XKFdJPNVqPjEHn5Bpg8kgxpJRDCS7fosfPd0wYlNC50z0I9pVuMWS83sPqasGp+0bZ02OlASWi3Gk31ywu7nk+Rd/zounn/H6xRfk1JNjJHnPtruk29zw1aePODk/5fTePar2pCQHZUM8MLv9L/Poh4G+7+j7Hucc1orzN/C1ym3q9bw5BzlpDX5d2WIfGCXbVfsMcw6ae6LDlLFNM1T7KnQPH8UofZtxHGWx+Ena7W5zWymFD4Hr62s++eQThmFgu92Kr15xG9hut3z66ae4uub07IwP3n+faVc5DFLWOZwTOT9tSuCb4P18GN7e+Gq/l33z97/jtVcIxGmtLU4ToihkrS2i2JqQYkkCDDFFjLWcnZ2xXC5xlWPoBmIQKauUZJ7Qe4/SdhakkGpcUzXLGQ5bdz1Xmy394Llcb7m83XKz6cXuKytRsDGHvRk1KxzlLN6CclLFp3PX9fvPlWG5HWl0x7AJXMWXdLcjF0+/Qg0BkzX16gW7bcdu23H97AUmduRGcWqPCLXl+sVT4rghdde40uOPQyRHDdpibItxNdY6tInkcu6yBoowtfeRvo+MURGjKsmLIcXMdjdwc7PmaFUxjh5diej8tCGTM6Mf8eN42HGTTXu2cJrul4N7HhGtnx7nMFWeEv1SPU7fv3vPMb/2fvcrR97vjrL97vfLCaCfyVP5IJGdEIyCjE1CcYfHBG0rFLEYZt8R6WY/g5iSJEYhJXQMqKgZQ5RXNBpbV2hry94D4zAwGid+pj4wDL0ghTEWBO+b1V+nVsz079fHUyat5f3J+S4r8DsHQaZfxLShyCMvn3/J868+56d//I8gDqiUMdmRdSAbgUyNrXFuxYUSKZxXZ/d554Mfcu/hE1Ynj9BWk80eyvvaocV66Kunn7Hd3bDZXtF3O7wf6XYjNkV0jIy7nehKNkt+/Ju/xfnZfc5O7mHMAf5QjqHv2Kyv2Ny84tUXf8bliy/4g3/0H3Nzdcl2fcu7Tx6xbBuOTpc8f/6c7fYFf/QP/78slzVHR0seffhrGO3IRZztv+zgNx3r9S3X11eycRpL2+4DkTFaNl0n6gv7bH/fz8vF3Tcm9gESVfhLCqxUKAKZSt8oxkDOmpwVylDWVy79IrmesQiOYzKyUyn8KL3A7e0t2+2WcRhE/8+YQuq5e/Rdz9OnT/nd3/s9drsdT58+xbmKxWIJCEno//0f/UecnJzyve99yG//jb/BIeuS8m6cqUiuoa5aGdBln73/pdb2SnFyeowxhovXFyzaBfWileShZLTe95Cleu6HgeXK8MMf/ZCj4xMUhqHIjWmtwIuSyjiKmaXRWpRwoienyPH9d4nJY62hrlvaZomzAe1qhqR5dr3leuexGpxJOKNYNIaToyOauiaMBfLTSqrDlPBeZPpevL6dN2CdMz96vcat4dml5tlmy826Y3j2JUpXGFPx+unn1Msj1qsT+tsbah041pakjkhkXv78p5g0YFLPQinQDpMMqjpGu5b66CHtakG7rIFeKowEFEHBmCOjz/Reoc0C6ww1EjxzCnS7C66vr1nUiW67w6mK2lgwslYymb4b6Hb93WuWpVJUgJrEqA9FvnMs66UE3Snpm/+eINASAOdHptdnisJv3Idv7Itz2bTf/OcAWL6e/p20PyfSjDrwJy23IVVVUVUObSX4BR8YtfT0tILaie7zFJQmdGAIgZgz690WVzts5UQ20TkWyxX1YkH0nmHXkXwk+sBmfcvQdfS7LbEIsExB/c3k97Df/00ff2qVTa2Q77J4v2MQFJ/f6TdkDComcgpcXrzk4vIVF+stPk0DrxpbWSwGR8L6hIu3VBlGpej7DYMfuby64Ec/rmjaBXXdEIMXZldOczNeKU1IHh9GXl28ZNdv8GE7SzCNwyBVCUgz1gf6fuTi8jVKW46WZ3KLHSom5cwf//Ef8+rFV3z60R/Rqh06diVwaBkczUKmGUaPqwwtkXF7y/Wrr3j6xSfcf+f7EgTQs/wZvJG5HV6Rv6SdNSURn16vb3GuwvuxVEq5MDj3Ae/NSrCcnbkSzHmvdzhJxc0LnH1eoUo1mFIiRWaLmHlT1Pueo+hqiaj3UCq/hJjLmsrh6gqXBNZ+85TlnAijZ+wHtus1H3/8Mbc3txwdn5Bz5I/+8A/5/NNP+OCDD+ke3COFgDLmAJoq75cEWbJQUukdz2n229d0+Wvh9Q00IE+vlb/hOUDO9F1P3TQ0bYs2Zq5YvQ+EWExXkeDmrKOpatq2hQyjH4lFBiunLG4Xarp3paL23YY49sToOTo5gzRKv2r6/GRC37FbX/Psq8+xWmSzJmSpqjTHy5bVouXdJ+9hrawPM+FXpRd/2MsxCn7tuBHT5PWan213+HWPzQmtElpFutsrEdzXIoUXc2C32TGOS1LSQtnHkmmwrdiD2UVD1AuwLaaqcU1DvWjxvvC2g4iRk7NoqGZFSgptKqra4hrNcnVOSp7NNSgtTubkiMpZNsVizK0r0S+um/aNi55EYkUhRgIzFqr2cOh07e9ULjMc8ca9wcFz5J8320J3dpTDnnQ+eEbefz+VeysXKJSpl5/lnp3Ggw5eFWsNzlmMM/gCvw/DUNxsoGmqGT0K3qMKcS6VP13XcWsMKSaaqqaqKhZtKHuFwtUNySZMFWlywlYVVVMzDj3Re8a+KxDtQV19QMr7eqvm8ASUcZec72gz/7LjOwTBg5kuVfKTLJYWKSR23YbNbsd2CAwYIhqMpsoVFY46eZwK5DyiUiLkzLbb4hNs+4En7/9QCAPGEkZhB6YYpedUKOrDONINHbfrNf24Q2nPGDwxjATfzSLNIUSGmNmlkc12y2K5K7Rd/aaKABcXrwn9lp/99M95fF6xajR10zKOwg41ZW4shCgEEyKsd2xuL7h48RVx7LG2EoudfX7wX+ohBFcZSRgK41IC+565qA8C3pvkDTUHikkCbnrO9IQDWPAwviuFsDGFpTXBcLkEFaWKsXAqmWSSNxtjEOknMtoYjLUYZ9HeYuw33KIl40sx0Hcdn336KRcXFzIrlxI/+9nPeP3qBQ/u3cMPIqNmiqhxytP7F3o2We6z+X2+cQ2/yyU91HY9OCsHf7/xIQ6O0Y+4SjLnaWNMOYuofAiF3CQwlEhWVdR1XeA+z+R9N0lnTXEwhWIXNQ74oRe5tDhK8JszAqkIUvTEsRc5M20IqdQqStwxul3Huq04O39AlSzWapzWRaJNRpQOeWcKxXlT4azhogrY4MEPGJUx5fcO/VYCW91A8hgCPg+kOJJTJfeXtmQMuqrRVmMqS1Y1ylYYZzFVha1qYhpJMaG0L2zwOBU+5CxB0GmNcY7F8RkpepK/Rau1VNFKPCk1UnloY9DW4iqBzO9gA3liHu/hUFkPkzdpgY4nuDOr+fHMviacYLxDoHX6+xuwsIPCLc//5INvzmGhwJ4Z5sA3B0H2cmpfr7jMjMCE2YpJSEFiYCCi2845hoPfNf0ZB09vxEA9hkiI4kZaVVUZvyqseq0xdYUyGuvENcWPg3hMTrKT07k4aNPwxu+ccrA98Ugk1N5kln7b8d0qwYM1rnLZ5FIk+4S1NbpaEuoTUr0UbcbKMSqLR9ONWxoToU7oNEAc6XZrtusLrroN55/+KQ8evsOjR+/R9yPee3a73WxT4qqa282Gq/Way9sRZS3LoyMIjuR3jN2GGAd20dN1lmhqdL0gZYoIryeHSB77/WdQit/4jd/k/Xce8/6ThywbcDqT/JbN9Us216/wu41Umn1P1Wiq6Nh2A7cXzwghcvU3/hYn999lcfqYwpfk7XOQfzaHkE5khMUYjfeeruuEKVpLdvZNzNC7hI59BZdKP2oKhiXFnVekLs10MeSURRVVOIBfQLI0UbWfjHfRCo0sqhgDjAplDSpZqBwqOHH1feOGds5xcnTCsm74crPl//nv/jti3hwjTW0J40i/2+A/eI/sO3IcZ4uXHCGrYu48bOn7Ldt+R+VXmDgJEd/tr7zdIT93V0hugqXefJWv92Im+rew8RQxJja3oo1qrGwSIcqaq5uGxWLJ8dExKQU2m7Us/izoidZCgjHAbrshjAMKTUjS5/NJMyRHx1I2kBRQKdFUmtPjlt/8yY9YNEusqamqWlCYDJ9/LjqgT5+/pqobqrqeN+GUFU5rbjfDfAYi8LOx4rhuiPfgnccj1u142VlCytLbVJkwbLm9CAx9z7JSPHi0wOiA1p7dOGKbFa45LnSrSEZmK7UF4xC0qW7wSQx6cwgyWzmZwhbvzOXxOcpUNE1DNg1xHFi2x9Q5UlnF0aKlcQado+gcu5pqsURRsVjeHl4sKPNvKKHPqNJvnYT8pVAovSz2vaz5sbKGBD6d7pfymFJfI4cc3idvfj0zP9kHh/3XB0EKpPor2d4kqD0dAodKb3p6OynnMpeaMUrDoqV2FUdHK0LbSsExDIzBi6+f9wyDsMFVhugDfhipnFipmUrEsmOKpBBwxtAsW9rVAj8Mwj7ebAnDcGfV7F03SvE9IR9z0rffvyZ0622P78YOnc542dySF5ZgGEe6IROypVqc0hydYJqG9mhBiooUEq+f9QQgKIc9XmCJ6NpJtQes15dzA94nxegD682WylnxoXOW9W7HzW5L7ztRn1BLFstjUtWQ+h1huMWPidFaxqwYhshNN9D2g5guKl0y4P2JOz4+4eHjd6irCsOIyiPD7gbrLMpo1gmUksxGa4/SUNc1fuzZXr3kZ3/8ezz6YMO7P6xYHJ2LmsXBOdv//Zd7TLYuUv0VCDEGbBKCTGYfAIV5lWXuRlEqkXQnw5tuNmPkBr+Tcc7H3cU33StTJZjzVP0d/ESpdqY5JG0MxjlcXaNHLzDmG0dVVTx8+IB79845Olpxe3MjlWTOhMEITT5nNusbXr18wccf/Tn3Hz1mVajw2kgm2g0Dgw+YqsXVC1zVsqcvlF5MgRXf4ozfCXt3Hz08O9P376ZKE/EAEPPfiQ1XNlQfpOcHxUjVyIynQuD/KROmUMRzykQvTOmcEspVXL56zcXlBatHjxmCYcw1jTUcVRqvM41VYrI7DmStqW1i0day/jIsm5roW5zV1FZTO1tcx5kdGA5VOnKGy01H0pohKI4WBnKNy/cYkyIkTUwarR3GVvS7jtpkjo8srhBbyJoYInnopcrKgZwGbCVVqJpnQVMZg5IKS9ooBoUw2mOKaFNhbIV1NT5LIIoJsA5TGZrKye/NoQg+yPwgDtxBYID92kFNJtqASgVp2ZPJVKn+9o9NieTB3nCn9ycV45t7xmG1t/9n//U+8KX5e9PSTJNVW+aA1DQRmw6TMUGLtCkuMtqAzgJVx0jMEAo0WVWOunJCYEsy8B5RsxLQ9FrC7AzEwgtQUc9O9ZPjBkqVdS/QcxhGIXkdGBHskwU5RTqrO2vrcB6T/PUZw287vjsxpsi355SJo8f7IAy9PjAkjVucsDg5p1ktefDwjDBGxm7g1dMv8VExUFEdn9JUiqpryGNPDiO360tCGOn6jmQXjEHkzZZtTV1ZjIXt2LPpe4YwoGwLZFZHx0hPZY1XgTEHfK7ZDXC9C5yuO9rFDu8HnHWysA6Oo+MTHjx6woNHT/DDmjB0bG5fzyScbr1FIUOgOfegoGlaxusbutsb/uT3foftrmN5+oC6XmEau8//1QFuPx1z6f7P9ph7fUaXfVwSlnnIvfyJMRbqvRgj76GcPe4+QVw5CqxI1uy5uuyD3WQFNWefCeZFqQ++p+bKJaUo7LsQZITCWDFYzgkzDIW1efdomobHjx/z6NFjvjz7gstPPmXe+FIshA7FzfU1z776ij/8J/+EX/2x5533MkdHp1hXYZxi1/WMo8c1S1y7wjVLclE22rdG8zfGwLvc0al3sUcB3uwT3n0NzZvb3KT4DzCOIyHEYnYsP+1HYX5qVRRbnGPRNJIRlwp2IrZS1Hr8OEhPMIMylleXl3z00Uf86Df/Gl6vGGlpneWk1nibaYrTxWW3pY+ehbGcLReSnCpF21Tk1NJWlraWP2NMhJQJMTGGov4x3xaZy9stSWlQhuOlZtnWHJ88ZkzgsyIFsUerbEW/3WJyYKU9zoo5scqaOAa83wjTNSdi7GnQKKOBgIhzRIqJYglMepYNjEkSQKNbjHUYV4uDOxASKFtha0dbV+gcSGMSFMXKGIAK+Y25zoMgCFCqlHjg1n5YmRy2Eu4GwOnBN7/e31nT/bP/m3l9zf97p/K7m7TeDZD75HTq978p4m5Lvy8i84FKG4wtQ+o544vhbmUtdVXhvS+uPgmVQ1GXkvdujEWRZzcZRSal4g2ZmbkIWaArNIp6sWTY7vDDMPcCtZ44sgdnQcloyl4HRs28BbKgU297fIdheakYwjCSg2DF/dDRjQPrruOzZ8/57NlL/vjnX9GNHqUVj+6dcH52xvFyyc8//xSlNc2q5ejIcXxUk4LHkYStqBUxeW5vLvB6jU+K3ZjIecSPmnYhQ6vWeFrtUWPg5kVH2B6jjMHniFrUMrLAEZvLjquba/7k0+dc3Pa8c/6Q7z26/zUn5elIKaFsjULhcZh6SXt8Tru8YBsj65trYCDGgb4fyCmgkufnf/oHPHv2gj/76GP+1r/yd3ny7od8+IO/IguWPcHhTkVw0Oj9Z3WI03zJgqMn58huJ3OCOWeaMuujtCUkL4onOZOTQmeFznHCLElM7FC5D+auRhb7qxRLLyonlBIjW4Ggp2yzZLolQ5ziZ4yhkDqi3MxGrFeUFkPPuo5U9ZI3a+mqqrh//z6/8qMfcXt9zRdffkX0EtwXdUtTO5ZtQ/aB5199xb/37/47vPf+hzx49Ih33vuA07Mzzu/dZ7e5pm5q3n3/ferFCdXiCKXsdJGYLbZU6dwo2M9AvsUxeb+RmSu/OySJ/a+KIRFDLP0j6U1N/ngxSKIiVYPhycNHvP/kCWcnR2w2t3S9JI/WWSpXoUkMQ8/FxUvqdoWrG4wKPH7ygLZ13Du7R5cqTkZJDDMaHwZMFmNkayp2/cDL25fEXc/J8ogHD9/h+uIVN7fXNB/+gJDAS46DAYzVVFazatzdPjEeTU/KYIwTRurKkZjWhxFvuNrivSJ5D11HToYhgE2RvNuQtmvy8RluseTo/kOGYWTwkdv1Lbp1mMZACiRiqUgSwY9cXl7z8sWaFy+25FihXYM2joqKbDOdrThZae4dS5WYhsw4QtPIELdRhlg8HQ+PWOB3ypqYNuFfFATl+7zxGN/6tbz211OwfRU4MSLhbr9v+rk8f/9OgCQX+6iMD4mDcMput6NOFVhDLPJkxjppdcSEj5K0DuPI6dFS0IAQUcowGk+IAa0VIXpC8POITy4oRe0mb1l3IMSRxJMwZwmGrsK6iiHsyr007T/54HxIorMX7dgz2LkDL//y4+2DYNHhizESR5HD2Xa3dN6z63tutxsub2558fIVm91ATpn11Q33Tm84Xq24uLlCOUMzDuz6gbaVngUFO4bCJoyRoBWDj1zd7BhrS1tZnFuQyYjQiBEn+2Fgl25Aa4aYyDaALS7V48j19S2XXjyqLm62PDk/L1I733yCpGFrZgJFjAmUwCraWGJgNqTMWVRqRj8wXr9m6xM/e/CAbnPDarHg6OQ+TbuaCTN5gkX+ksBRXQw+gbniSyniC9nERTFJ1SmhkyIpZl+10vBlprvqOW4xLaw50crT+ZDgOj+eOIBdSnWZ89wrmV9RFRKClms71VJGK9E9tdU3Xq7DWUdnxDg5p4xWsnlV1gqE4z0Xr16SU+bm+prbmzUnZ6ecn99Dq8jp6SkPHj4qOoeZcdzNla1zTtRviqzG9Pm/BnLfSdEPH5SE4evw+NfBrqknOG2s0yZ6uNkaY2nrhidPnvD4ySMqZ0s/NhZCQamGPIShZ+y74nNoMEqxWi1x1tA2LTlolvVISIHOj3TDyBgCMWV8jPTjyHq34bWP+K5j0bYM3Qbfb9ncXhcIO9HU9Xyfybm/+9kURRhByT1TisKCmMn9ZgyiBpMFhtMW0hiJWSowUaSpoaqwdY2tW/oxSCvGe8ZhYOg6gdFiIPpIGnb02w3rq0u69Qa/G7A2orMM0sv5NUw9fE0mBEXwMI5Qt2VmFDisrKbjTiXIFOS+rRIsaldfC4J7yFQen76eiIhv3ClfqwCLoPoU6OY1xwyBprQPiOkgCIr/62HlDjEEgpffH0KU4rqgf0JCEfQmRGGGWq2om7pcj8TkKpiSSPTZbFCTxq/WOOeoJ6KROpxH9kSlICWccwTnUMM+4Zx2ToXsVW8ial/nNLz98dZBMEXJVFOI9Lstfbfl5c1LBhJdjLy+uebVxQXPnj6nHxIhZJ5+8ZrKGSpnqI8aXOuoVzW/efsBRwvDymp0wfDHEARSz/K2uu2WTz75mJNFxdGyYdm8jzYV1lS02hGTZzf2rK9vGEMkoqlWFrOw7ELm4sVLPv3oE65vBx7ff8B/49f/Kj949z1WTf2Nm6pWkq1opdBG48eR9fqWGKNsPosVu21H9gVGVJqoLLZKbHZrbl4+5+biOY+evE/qO379r/9Nnrz/A7RdzjemwnyXBOUvdAiN+UAAO+ci/SQjqVXdludpGSBGEXVCZz3HKwmAeYZocnktaRcUm29VmKAzJCoLLqWyIHKa4dSJtlyWnzhLW411DpQmE8tiloSkqhqauvnaKYtJHM/XmzXb3VZm14pB8ySRZ8pnm2jbTz//nK/Ul3zy8c+p65q2bXhwfsx7H3zAg0dPODm9hzOO64tX5CL0fX7/Pq6We24KZd+qDjRnCjJDJopKZewCmUOcN8xvyPBTkoCnddGjBGKQzLuuapaLJQ/u3+e3fuuv8+47j6mqYmadE37sGXMu7vWIMW8Yca4SNR3rODs9xRjDcnWEGT33hzXbfsurm1v6rufsqOMkjNzuOrrdhqv1DbvNU47rGsvAbnNLGHu+/Owj2uUJq+NzPvjgQ4ypZojr8HMppv6lmSFVVCbHQNKxpBOOYXD4zrDZGRSOVb2CfEtmpF0e4eol5iiSlwuoK5RriXmDjwkXAv12R8qJ28sbcgyQImlzSb++5cWnn7J7sYObSHN2UoBoW5IxRcyZNA6kLrDbRPyQGXawOqlA2dkANr0BG+576cJGnIOb3qMmX68GOfj6Tcbj4ff3t9PhvfVmID4c85v2GNg/b4bY8/7rXL6fCxs7xYO2BpkQhPeQYmAo7NAUM0aVOWIEfh+LQpB1jpU2dL3HBxkxySkSQhKhDmtRTU2tK5yraNqWuq7nAfrp/TrnyCkRq5rYDeQoBtmq3NNaIfOY5fwkvR8B+YtuqW8fBH0mDIG+2/Dy4gtubl7z+uYGqprkxD37waMH/OqvJW5vd/hRxHBTKtBpOZFOaZrK0lSOiogp1YN2ofwmzf37D1mejFxtduTkMVYxojEpodIg1VxO6MqhY4VSnjBEbNZYHEeLFafHPffPTtitX6DTHkoSlf9fdIgix2qxYtsuqKuG2DSonEjBEzeaEEWXtx8C212PDxlSotLgtze8/iryn/xH/z5ffvYZj9/7Hr/213+Ls/MHnN57CFoGyQUcm6TI9uX93X//YsdE/pg/mQJrHa6qhK5cqMqi/5pRJPKEFM/BSlqoWi4LmAJ7pozSIg9AVvsAmEs1f1ARZURPVj7VBB9lUpYZK6U1VVVjTMLaiPf7hWedlQD5xjnx48jl5SVfffUVL1++5PzsjBSlyd/vNpASu+2OppYZQ1M231Su45gjyfeocU0ce/6zxYrPf/4pi+WS24uXAvFpzQ//yo+59+gh73//Q2xTF3r39C4ONyR18E8iRRlD6Mc1w9DRdzv63QhZUVUNx0en9NubO68xVdKyOUqyJMPueVaSGceR9WYjTiAxEKKm63t2u45+FPhZ+iQJozXN0RGmqdGVo6paEUaoKnLWKDSNqzDWgTFElbntel5d3/JgjCRtqRZHPFgsWRoDYWTVVCxry3Y3CIFsELjKaCE9UZKX/RnKDH3P2BmcURhnUdZgrWPMCh/g+eue69trLi523KxHrLE8eXCPHz1ccH9V46Mm2gpTW1QlEoWhH0loEeNPme3NmuuLKz7+s58XbdXM9955SO1Ouf/k+7x4/gXNq0s+uNqR4xqvL7kylthvid2Gvg5svOJmM+L7Ed95HqhjIYbkSTXp4HLnXET1JQjqqeenJLn5RRXh9P9TAJwg0rsBcIZY7t5lc7W2f3xC66eEVR4rlR7fAIN+7f9zGW2Zbl9FVdeiMUxGFXIKMWFrKWZOjpbUlbC5fYgYC1XTYOxdF5eUioB2Ibcsl0uZCaxE2ck5t98LUmKxWEjCHiJxEDaq6I0GiBGVBFXRWRV2dxaDhPm8ye+eFHK+S1H4HStBTzdsWG+uuF5fstnuMDFh0NjKsVguuHf/HGtFgsgksW8Zx5GwGbHaUFcOZ6yojccpyk8wiljS1HVFTJqTo2O23ZaUIz5CUqIN6vVUmRT40hSngKwga5q65ni55MH5KdevrllUltrawg6dsvGvH3I/KrlQrsI6h3UVucBqqkhJhUImkUFmyao0mRhGuu0tX336M/wYuHj9mnbVEj4YaNqGuj0TcWZ0EbmFCduf/j2ERv4ix9x/n2EDUXdxTjIybcweLp0WTiyLp0S9aTnlvO/nyRzaVN0pdNYzPCxttKmam6gzk/9dLoK+UgfuN3xd7HhSCcqU9yTsMf0NQrghBDbrDbc3N2w3G1aLI2GoxUQYtMzFxYDKbpYKS0VyS2CsSPQZ5SVYf/bJJ6xvN9R1ze72Eo3GGoetakY/cHS64ujsjKpusM7MsO4EMZGLyn5KhDAw9lvGccd6c8l2s2a9uWF7s0OjaZsF9+8/5vr1xZzFQ6kgtC4bm5p78KoIDKRCKw+FQJTTntTkvRdh8lzaBVb0Fau2FbEBY7CuwlaNCCaUjboqQ/e1q9gAvR9Y73ast1sg4kOkWjY01qBypnIWpUX+Cq0wer9mVM5CcHjj8B7GMYlCkJK7yljDGBLbEV6+6ri4XPPyxTW320HWHYYPTmu0tmSlScaSnbh6pJyKe4icsxQj4yCJwMXL1yK8kDL3Th+hjlpsY1H2FSorTgcPrmPcrumtoR869NiRsyGi6MdE8AnvSytg0kp+E5KksGELESxPDMaDGcE9q1EVJEUVgtrUt/p6/+pbN+6SV85ribtB8G71R0Fh5Jsp/+JAGA61Q9WEIIk037RTagVWK5wzLNp67uX5ELEpCUu3WKjJ6maWVQPwhYehtSn7j8wZTm2anDNVVcl9biNVXUvfsG6IfiTjIQdBGabAN2+Z6mCPOwSS334PfesgGELHtrviy5cf82L9ik2/k16ZMjiMWG/UjtXJEussMQRMjHR9X3zsMqujliePz1hUNSpBt7tmWWkqq3CmYmLNvX7xgtErlvUxNzeRbTfibGZRKRaVBOQp8/LBCpNaRYKPmH7k4cOK5fuPeHR2zoPW0VYN3398xqrSRD9wZ/c5PJRo5hnb4OqWul2QRrG3WSxXbHY3dH3H5eUVIYhOab8VJwOFwrmKjML3az7+09/noz/5Az756I/49b/+2/z2f/Nf5jd+67/NYnUKtsxXTQrz31RV/AWPabGglGyAxtI0LU3TUDcNrlj2TCojE7srG+nRWDeVhZmUZNMx2ZByYEL+rZbgOjEQcy5iBNmgdFUCoCYnM6/waZHKJ9Yi46YVxpR+c5QeRSSVgWX1tVMyDgOvX76k3+zwXc8mT0zULK4GWuOUZtHUgFTvIRePswJjxRBIzjDGHduPPuHnn3yG1oZFU9HUIry+3vwDzj8+4/nTz/j13/ptHjx+zP1Hj1BzX1kG7ePoubm6Yre55eXTL3j+/CmXlxc8e/olt7c3XN9c0XcdRmlWzZLHj9+hG8Os9amUYrUUtaRhCPgw7u2ryoYaQqSpFXVJ0LS2jIMnhUzOiuDFLzGHgF0taJqao6NTaieOEE1bo20F2pHSiFaaZXvEr7zzHn235eryNbt+S0ye3/0n/xnTDNm9n/yExckJ9WKFsNwTS+doT05Y3XsMyogPX0kKYt5n5zkrbtIJdqxo4gY3jlgLVYh8/rLj6eueP/iTFwz9QPQ9pqrIueV6fc2Y7oOz1K0lKhFnjn6EQkhzpfrs1ju63ZZut6EisvOB1zeej17WrLpTlo3jYvyKdXqJ8iPHt7csBw9hi449r4dbjt5/j6OzE6KuCMkTYpKxEKXIwcOMZOyPfozshjjDnhLx9hv0BP3fIcMw3fcHkHoJjHNVeED8uLtNTX2/NKPu+zne/ZqaZudCTnPQm/xAc0meJFmVNXO92d35XHqyBCswpDKaqnIsmpqmrbl3dowPkX4YudmsGUMsif0khCEEmhALeqZFTm/0onp0dHwscKgx4gJzEAhlXEpGMTCWxfEJYRgIQ4/fiZJMDoGsc2ldiYAE6UCrOAka8h3IoW8ZBDN0/S23W83V5oIhZ7KrscUGh5zww8A4dPTddpbByv3IGDzBB+rKcLxqefzgHk5rCBFnwOhpJqclJ00MsLvdMsZMzo7KBVlYuZPvew0Fg1fakIJsJikEUlksl8+ekrJmDPDwbMHp0SkPT1e0zrDx/ps/o9p/obWU3DGLLmJKiRQS1lVUtcySlY4xVuvibCGSTlM1VDlFyorN7Q2f/ezPS1Zkefjkfd758Ee4ukVbK1mLUv+UQt/+iEnktpSSxayNKZWtE5p4oXwrJYQnVaj2IhsmMla55HWq9P0ie5aXAmGNUm7iJM+hVIrSPdhXSgKtHsweJn2QGZfn5BIr1ZxP/uKEBVmwUt2LkHNKgQrJyEV7VNxJQpLNISXwxesyoYgYGSq3lroWyyEfPEoHlPZUg2K7WfPqxTN++id/zNNnTzm7fw9rrOhx+hE/jPS7jqvXL9lu11y+es769pbddsPV9RV937HrdmSSvNfB8zQEtr2/0+MZxpGMYhxk5k8rjbKFOBCjXKecSEnmsmII9N2OwUsC4qwtvStFZQyuOKdMiv9AgaynQX052b/xgx9RO8dnT7+iGzrGoefajzPEPYwDIfgyVC3beMxF5ccPM5IjEHe685lShhdXAR8qjtyCswaiiqyvtry+6Li4FLUl02pU66iXRwLX1xW2chhnqSpHzBkfAzntyDGgowddCZFo9GTvUTFytGiJybIdRl7fZl53I84qLrcwZs2QRZbNxogde2wcsCRsUaQKQcSgY07zcHYMYd8fZz6N3Gx7si1O81MJN1eEoDkMgvD15FbNReCdrlaeus/qa1hoLkHw4JG5DTGNMuU8zWvGOeCR89yfnwUsCjFmu9u7yssrlvssRpFQs5bT4yOa2lJXjqZyoMAHIyz5rKibllQkLlMueqIxlfEHZKB+HBn93pNy4irEGAXmX6/JWSTadn1PSAlX1XM1SpaZ1zAoMnHeG1SS/UiX86V0Ubn6Z1EJdv0Nm13ientFVCuwNaYwfnJKhFFUVbpuy9hL4Bu2u5koUVc1x0ctDx+cSfmcpiCY0SphjCWWSqLvepHbqSqqKqB0RNGRoyZmjUoKrQzKOFIYSxD0ZBQxBa42a7JWZG24d3LOo3unnB8vqZ1mc5Cp/sKj2O2IQadcqBQSztVUVYPCkLMIvpqiaKONBWNKJgRaO1KGXbfjq89/zsWrFyzaFd/74RUnJ2eszu7jdEvWalaUL9f6O+HZv+iQnlHAFONjsUpysx+fsVZuIgRGFMZYIOZIUpNTepYsWJXKO+9l11IJcgpVApDoWsrClu+kAommJJWCEGvKG8watGTEh/2L6ZgW7CG3cn/IpjXNNIHYOMUYQBuUpgRBTUwCXcUM8RD+KZ8xa4uxjqZpMcZwfT2CDyg9Uo+w2265fP2a7RhwTcviaEllLVZrxr5n2HVs11suXj2n221Y314Rg/SLhnEQyDIGbOOwSqEGz3azYb3r9xtaFrkpsmIYBuq6mX0ZvRclDsrmNOk0huDZbHcyXwlUzhIKDOy0wWojBCEt+pv7TtGkeSk32l/58Pus2pbf+YPf5auXz9hu1/gwFsRXMY4DPgZiimRVznUq4y1jP9UwgGTkhzJcOcPLK0/I4I9alsaSlef69obrm5Hb9UhTNxidcSazODlDl9kyW1lMJRtviB5SIKQdOQUxSEZBgug9OQR0zBwtFqQc2A6WTzeJbfKEpGCbqbKmw0sSlCLKj+jsMUZ0JpUy+FAqmJzm+VqpUtR8rabrdbPtiaZi386gVICqBME3ZgPvVHaTUkxZ8/Oj+9cn78PmTMqaFso3/NQEkaeSjMQUBSEryUwuATOVf3X5erfb3VlhUljJTHFdVyzahnvnZ9SVEZGE2hJzxpjIthuIOdP2PdPYV0pZ1lvKGCdjVyFJEBzGuwXIJJQxjiNdJ8bbfd8TvbRX2rqVAFVORPCjiDGU3Hi2hZrbNZQ9TX2nTfStgmAmc3X9iqbqWV8PVMsVrrL4PDIMnuAlQyUFGMXwciyD9FpJmfz4/hmPH5zx4GzF8VLUKGoWGBXn7pMxGt1anrzzkBBHer9BbXr6fkffDcRs8cqSs8Uah6YleRFe9d0gHoLGynCmhuQSJ2ennN2/R12Lr9ovPzlyUxtjqVzNFuF0u9qxPDohBI+pFoyDJ8QRUzZRVzd0oy8LyKHSVC16xq5j7Lb8p/+ff5+P/vj3ePrVp/z2f+tf5d0Pf8DxvScAhaCif0HW+F2PzOhFJcfVjegrGodoO4ozhM0yamILKSKFzOh7QpDxl82wFk3WMFIZoTYvl0fYusFYR101pMrK5htD2RRHtK5QaHzYsOs6uq5jGIXMVFUNq+UxztZUblH6VZpJg3FqkqckYsYph7nHeHi0bcO7777LvXv3eHF8zLbb4LTFWCWGn0auV8oS/AQ2Dwx+JJJxlaOuZSY05UQ3DNIjNZrb7UY2KK3puprT02NWpyt0Gtitt/zu7/5Dxm2H70ZIEZWFMzTbTpm8V8oIMv8UYkQ7S1IKX+CfcTyUq1JUVU1VC0StlRbNxnEvbqC0JA2RRNfv2Gw33KxvMcbiqkasfpIie1ARdNZYV6ONAy3JmTaSPPh+EIskJ/3ze2f3+Ft/9W/wn/5+5Pr2hmTtrOwTQiaE0vsqCa2IJ4/s1tccn+5wdUbmQ/MbMFQmjIFd78Fo3l99gGstZmxZDa8IesNisSQnT/RdcTjJLCrHUVPT1g113WK9RqfIoIV3a4wjZiVoQY5oJSbFy8URbRs5anviZeT1dseXlyM2DGQV+FwPrHPiOsKN8mxcRq0akrWEoNhueoheyHrGgtZ4L7q2Ih0mnyrlzMXlBbthkFhcJrb1FAT1ROcvprVlTU99cpDGz4wAqf3OswdHy2OHVWimkNjmOpBMYbYXBuuEtsScZLavwKESdCeBePnJlGG7ub2DtgjrW16jcpa2qTk5WrFc1DKSYxWJLZtuYLPb4UbPYrEUZ3lnD5xo0txTzRm6rme73bLZbGC5JDtH38tjt7e39L34wvb9gFayX5mJxGgddqEhWLzKpLFHJYVTMgaVYyT7OJ+7KfC/7fH2lWC3o+8sOSqsErglxTx3TnO5YjpFLLl0Uy3GqJJBOJxRkDy6gAVamYOGcRYDXhI5BBKeGHpszlTKklQilwx0kkfSKpBCFA2/FCFKT9EomU3BGprFknqxFBFe9e1WR3sdOo0xTgx/jZHUN4vDQdXUnJzfn0emh36UJrmPopiRMhRbj5RBawsINNHvNlxfvOCzn/0p9x8+JEbPj5oltmoxtnrri/bLD9kUrLPzhjwtmH3TXmAJYxQ6G0KOxDiw3dyw3W54dfVMRMTHgcY5mqbh7Pw+q+NzmsUSZyq5BtqgkxZJpeAhR2LM3K43XF1fcnNzzW63E0uVdsWDB++wWh5zeuqw2EJ2QKrNAxBjGqLNb+qszddnqkpEw1Fuw1m7RcR/8aUfmKRKSomosrg4KakQcyxVQS+N/dFPYsGKnZaAud3tqE5aNDAMHd12y7gV+rZBGMVq2rmS3C6i/ykQbEoyDI8Ggy7Q7GHVm+l7cS+ZFC/uzFdSBpW9l41jt2WxaOeeD8ggOjqQi1SYQkhj05yXmTtSUi2oouaDUjjreHz+gKPFCmssY/LksqHEJH1a6S3JRuOsEbUVPxZ7pgrtXJEsu3vUtcU5Q8qZbR9JGHx2GNfQtJG2XRCGjmHsyd1Y0AFQxbVAFZFzlWJx+UBQirmPJvJcRimROlSKnBz32oAKiR6IeBJwoyDpRNKBuDBEZ6lWNbaqUcYR0DNJTykFWZCLEDM+3qmX6PqerE25fqpUgfsgOAtxq9I7V6IoU7YS2T/UdO3kZ83UFyjrlPlTFq+9LCLfitLHz8gfJvWlLNB/lkAd41QZ7gOnLveAKkEw+PHuvlFaOkoxJ6Qp5VnKL6vEdtfR9T3jOJREKdAUYktVuf0eQ5aZPvKcJHddh1aK4By73Y6+F9WmiWg4jh5FRKuAs3YeFaIgPTFnUBqjs1jjpUTMh4Ev73u0b3m8dRDcbDZslxaTKmrlqI1hjAmlDNlYcukLmBhprSZpja0UlStYcm1QRIZuQ2pdGUq3KC0nLeXSizGZbrNm7Dv67RqbKxZqgbULutjR+14ylSALI/lEjlkG78t8V7VYkipHaBzt8Rnt6pRcPP/yNyzUrx8aaxvqZoW1FSFlclE/qNqWdz/8Ea9dzZU29P1LhjExdh0RYYoZzIzH26oh5wjRQxrY3l7w0z/6z8nZ8/LZF9y7/4iTe49ZHN9DIK99+3y6kP9F6sJ20dK2bZnHUfNiEDjTFAhXY61C6YzKHj/ueP3qGS9fPOWjT/9EvBqHnmXdsFod8d773+Od977P6flDFs0RWom2ospiTByCZ+hH+r7n888/46uvPuP5i6+4vb2lrhtOT8/5lR/+Og8evMPx0QlGNThTHEdKb5GywQk8m2SU4muH3OxjGZSOIQhVv5yvmCL9GMlK2LtjiPgY8GXeIyK9H4LAvCFFBi+Z/jT/pLUmbUaUgovLmvP3z2mcw5pCZEqF2awVhqknJpTzoNN80aZAOI4BazTOuAMR4/2nub6+IqbI8dHJ3FvThQ2J0ox9JxV38FxdX1HXNc5VMwxWuZrgEtgAszmwIcYMITGlWEKsKH0h5H04a/ng0RPOj09oqprtuJPqIYMPwhKVtoBH5cTqeCXmx34k+BHrakylJYl9Yxzg+KSlbiqGEHl1saFyTipVW7FcQtMs6H2k6xPxZjv3kNLoISZyHMD34Hvp/wBZazIyd6i1Qde6KOyUTTNH3lkNnKTEQg1cqoEb4LmBjclsqsDJqsZUjtViQb1YYqqWaB0qe0kgSlYTk2IImTHcTVq2uy0hTS4Sek6CjNZFwHwfBBe1fD9E8EFJcrxf3tSVojJQ2TwvfanwJn2ijCZiiVglurgxyNjZGDN9oOh6akas+CimvZ5redNzIJTXk6/9OMyfSylEZCLL/S+SmKP06ToR2xiCiKNsdx1930HORO/RSDK1WLRYJyigHwZBdEDaY/3Aer1mHGUU5ur6mnGU6QER1S49wr4vHIMCS2tVdGQlEai0wmqLMzBq4YykOJGUxNLun752aEYG0lOkbRR+vMWPa/rbG2yzxLZHnKxOSGNizSt8EjVwTE1lNLXNrBaGZWNpjcNmjU4ZZbwMfJKJSSxjvA/stmup+CIMIZGjIuYKU5+yahXJ92UoWNNkMCrSx4izCmsVMEDMGG9Zmoala+eK6K1CSgbnahbLI4ythBhAAi02S+9974e0dc1yuWK96RnXW3a7jjDBHcnjrAh/V9ZKZWhAJ+ldBT/y2c/+jKvXr7Cu5Sd//W/xq7/52yyOTimFwF8YEBWnbSM0e5XQOs/9QGvNPMCstRFdxCwq8evrS55/+RnPPvuI4GVOxy+P6He3xJg5v/+EqqpYrVY01uJCRI+RHDO1cVwNN1xcveZPf/oHXL7+ipurl3S7jqZpUeGW65Mz2qqmckasgyqHinu7ppgCKQVCCITgZwbl4TFn/0V7MHoJMGrSIsyJmALGGXKC7a4EkJykZ5giKoqriFaSvcYyeuCjl80ni45IP47cbjZsdrdUjSWqQEiBIXic1jJsXZwDMmLiKgXT1JsACryZSmac86RRUj4PcHJ6wmKxRGstfZEYS7UrrYLKOenpxETfDXjvOT4+YehHGRlQmmrRslg0LAuxJBWhYlMCZY5JZgitiKinGEGJQ8jpyRnnx6ecHx1zubmS96bUnJ0L1H0wf6pEqWa33RCzptUtWt0N7krB0apGGcN6t2O72TIYi05eyG1ULJsTmj6itcXUIvlmAOUFDo4V80yprZpSbRlyUCQdxRdTZczkVUmCCPXCkLVh9f2K7uXIcKtY9I7KZJoKTCM2ScpZxmEgqQ3V2Qm9h25M/OFHX7BaLTg6PcUZw67vD3p20O120gNXe1F5a3Lpp0vtVtmMc4m/9ddkrX353PCzzz0368SyVhzXiZM6cWQ9JntM7OijJSRNF+TaKzIqKVQhig2pVHAZchEg6HtDRP54VZWWxz4AGqXmnvDkH6hK0ubDYSUItsilaaXR1qC0uMVvdx39MLAdeqnIYqByordaOVGhMsZS11UZxTL0OeKVoC2ihx65ubmlaRohcgkEhDZm1hwVoXh5j66QcFCSvClEu8MUJMOPXljRMe43zOnf72Dl8/ayaSW71Crh/UCMmXE3QLYoVbGsHKFtOFkt2XbFA81amqaiaWuOlzVHi4rGGazKZVi8QF4pkcZA8iNhHElBNpIUNbudbIiuqWjqiqap8V1CJRlRMFFmVsRpQonwrpJsJ8VE9sIcnWv8b4kuk7RRBrSxVFWDtqJxV8ZiUVpTL2pWJ2fEEDh/+BjsNX28Yr3rJWv2fgbszcTsg3kzjDGzvr1lHAOffvSnnJw/4N7DJ7hKoBlt3Awvf+PbfYss59AqafqZGa9Xe0iUMoZgraOuaqmmoieNHSkMSBe6IaeA9yNaG+qqEdgkBPzOk/odKUeslTsvpMR2e8M4bEhxR44dKoKKC1SOGKVkHsxa6cXlTMZgTJ5VbiYZsTeVOqbPNgV0bcxMJJhAsqyQ8ZOy+EOYxjrkTp5YaalY+cRsZ1EHqZbL/VNg1ZnZZgtzbv4j+GdEoUtQm6DOaWB3YsOVfn5R9C9v8uDzSGVnifHrVWKeYMCDx4QcNGnDRpRRRTvUYitJgFJhGk7vS5VMWqA+yFO/EekNnqyOODs+QT9TQqZQzISnlHJR/C+vVyA5Pw4oO2C9R2upHA/CIFXlUNrKRpxE2dOPvnjWOXTWMmvc9yzGHuH+JfToiT4QUtG0VQqlbeEji8yeUkkKXibxZEVOAkdaC64Gu9S4VYWLLa3JWJ2wJqKslGvSu/UwDlRJZONC0Ly+Hth52CWHSp4XL68O+kyZ4MeiggM5G4xCeGSlzZCTojaJRZX48LHCOU3wli+eBlLMxKDAJSwJnUZM7nFpwxAqcjKYaGb0RmcwKmGViEhIIW9EuBtNjIaIIypxfZBkSWTnyJS+u1y1qWdIzntpyDuLi8K8V7jCJM8ZqQpHzzB4STTINMWA15o9Sa1yThjJyRDHEXIiWVNaBkL0UkAoAhCTmtW0ToACJ4vPoJxfQYWmgXk1vf/ikCLdNzW//zlyvuXxdkFQQb1YYesFu/U1w5gIIZH7SBjXqM2Wew+ecP7ghHdOfoOfP3vKutthjObkZMXx0ZIn989orKYxgBmE8WkSqvhQxXUnunFkTHXMboBnVz2fPnvJbuj5/g9r3jldcXq24oYdOXp0yliUXGRds2wdTW3xwTMGGELk9YtnqARP7t3H1s0duObbDlM5apZUbUuMI9GP5SIACRZHZ7h2xeMPfsirFy/5+KOP+b3f+wOurq/o+hHvR6zR1GMvxJRpg0sKUk0KA32/4c/+8HcZx56rV8/47/z3/0ec3n/M8uT+Xrv5lwTuX3SEEEXmLmmMNjhbUbkaZ5yYW0Lp4ViUtrjK8vDBE95/8i5hc8Xt65/ig+h2Lo7PqZtjlqcPuf/gEWfnD8gB1i9es/3qK9Rmiztasfjh+6wWx/QnnlVTUR23HNdLdreRRdvy4N4JHzx+wpNH73Byci6ek8qgzaRwMbncT72Wfc/r8NDaUDcNbdPQ1BW3ehqJUDJHpinBschb5SCvr0oPAaliUoxEndFG2JY5pdJXlD51yomQAv3QMww9yqYSTAubVjF5bZTFOv0pUO7Ux0F6aDEnAkFGVt6YPavqBqUt3XaDqyr0TCEP4r0XgwR/rVm0LW0jrvL90LPrdiwXK1ztME0N1oEugxBKkkLvfYHCpXpIxUbHqYgxov/4/fc/YDN0/N7P/ozsPSKKILJhMYthtUIxeI8PUg3sui0+aZJekjLcrLs5WCgFVd1iXcPpSSKZihAj29uednlMZWvs+ob6xVPqn/8590fpF+3cAvP9d+jPj6mWK7Rye0stuVHQOkovTZegMG2EWuOUBRMLCtJRHTtWzSnOL0l+JJWWSiKTkoKhwwRPs7slZ0tSC15vHWmbGJ9d8PlHf8TFy+d3ErIcB1KQXl4MUpkNKOEAKEMKirNV4r0nnr/2w4G2hhPn+OzzitcXlvUGclAMXqGSYekMD1t41Yvf48nK4L2MeTWqp3GBk6YgJGg6FowpM8YIOZC1A1NTW0NKgRgGRp+ICZIxGCWCHj5konQC0Erj/d0FFkOQqttajk9OqetaCFlFpECVJNoYS9tKy6WqxKPRuVpY56UPmGPCW4czrvyMIFM774X4FSPWWqqqmhVkrLXoSmTVFstFealE7BTJBxmej0KaizFIoXEQQFNhin4Xhv1bV4LGNmhdE4Mi+kwMWVT/LRirWCwUdbvALk5x98/YDiO538jwsslYPCaDTZP9hSYEmQVM2hIXlnGMjGPg6Vdrbjcjr696TGW4d3bCo/OW1iXG4ZacxBVBYFQZLCbLzehJkglqQ3aG7WZNXdVst7cYhWgLfutRIARtwFUcnZxBzmLzYoW+nXNCO0tlDUfLlcwPVg0RxatXr/j8i88Z+w7vPWPw0uT3CaNtIX8YQpDc3vvIi2dP8SFwfH6fD37wq/zaX/0XqdrjuSJ8Ex59m+urKHN0VqTH7NRkVhzs1sJsS1nIHc1yxYPHT4hhy8uX79EPW3z0nJw9ZnVyn8fv/SoPH73DYnlE2PQMNxtuv3pJurllef+co3ff5d7RCbWr+MkPf8z11ZLb25pLrVk0K05Oznn48An37j/EuoqQ9VyfTZn8tCjadiEV/jc4yyutcJXj+HjB2dkR3vesnKN1hu32ijElehT9IIzQTKRMEkHWUBTxJz+McfTFtieLtJfkm5ACMWZ8gH4YUAYICJSXsmTdhWAwdW+l2haRgHEUUeuUEqnMTHmiKCZNkEM5JpFs70dRT1KaGIVtPEHFKQk55vMvPielyA++/wO8LyNCKc5EGlHgURLAklSzWgnDUqcIZahZZrkEZrMp0VQVx8tVGYGRJCkk8DHjg0D82UiviKRwpoYwopyIWaAr6rqa79CUMy9fXVDXrRDIVEcuVY3Go5IiXF9Q726odWJ5upDf2WXiuqO/XFMdNyid0ARsIT2o2V5LrpPKe2Uj0GArFCNaR+o80piaWIvaTTSKaMVoem9umUhKs729ImZDzKa0QQJdt2GzvmE3dvvLlYWgF2IxiWaChxWohC7wXVMl7h0nTIZKZx6ce773RDP0mWevEqs2c9TCcRs4aUfeOQ2sXiqGMXK89CLoHSCPA+fLng/Ot2gViGj6OPLzZ4qvLhQvnmXcQvROfRQHFz8WH0kQhauy5kXYQCTnlk0FYU/Iyzkz+iBojNbUxYA7hUgosLhPCesM1h7C4uJ0Ym2iXbSzElW366RK1KYoQIk6lC9BcIL8rRVvTLm/Bdq31rFaraTqi5GbzZbsR7IfoaxcmVG/i5yIQpSaJdze5nj7IGgcWlcSBIM0ZrWxaAe2VjSNZnlUs7p/jjm/R+89/voV47AT+nMapCGOImtHSloay1qTtGK0lm70bMbEl6+23K57tlvPe++ccH684GzpUDng+54cx3kmJhR1DZUVwSdUMjJkqYXpNAxbdtuKrtuyqOuCiX9DefHGoZRIZy1XJ4Rx5Nq4PeMpjnI+jGGxWlE3LcvlEUMMHJ+f4mMQVuR2y/r2VliIPkIJREZpUhIoK8TE1dUFm+0Nx6cnRD/wzrsfcPKwomoMqD2vT97X4f99+/vX2oh9jbFYa+5WwaVioYAbRilcU3N67x45dTx89A7b3Zph7Dh78JiT80d8+P0fcnp+n6ZZsL0dCN1Id70mXF1jXI0OcNysWCyX/OD9H/CigaqCGEbaEgTP7j3g5Oxc5sFKn4pC5pp6fc452rbFe4/9Bj9BpRTGahaLiqNVw27XcFbXrKxFhTVDFjWJYewLSzeWzFA8yybaeNYyl+pTxFDgy4kUkRGR4WnIfvQYa8iFkqdKRTLB1ml6X0aspCTBmeDPXEhTmYDYzrxp6CUGrRT1nQpULpn33oJnmqt6/vwZxmjeeeeJQL1pD3Gp0mMBREgiRXIo84IpSn8t6XmeyySZA00pURWPwukuSaiZYDF5Tio0IQik7awjlNEoo8E4Q1XtzWdzztzc3tA2QZjdxXKo0gaVAjkkwu0F9FucUbhVCwnM2BO7gXS7Y+iDIEZ4ahWl4tfSd8vTfVz+TYU5iBHfUKUVLss8cmVkxjUY8SEUpqYMXU8BdNht5XEUVlf4caS7vWUcenw6TJ6FxKVjqZp0FhcTpCcpe/JIZeFkiViTkTlqPY/PFNu1JvrEss0cL+HhSeB06XnnPKFNYOgjRw3EpEkRhs7z6KjnVx6tsU6uQzfC+kbz6qXm8iKzio7lcklAib9jiDNiETN7lnJMIhChRQs0uLt3oveBXMTe9QRhhih8jRAIWUZtxHJM4A+p6gI2JhZVhbHClpYxJGlTiN2SrG/pYRcx/vJYVVXkDO0ikBFEqGlaVIqCSkRxos9RYHeluGPuPe1pSlEUY/4ZBEGrDCYrYucJXUdKkcXpCYvjBYvTBbppSCrj+7U04IeR3e0FOXpyDvSxF3NNY3DZFllwK8ouMfHxp8948eKSZ09fU1ULjo6O+LUf/4CzNrGwCb2+YhhH+mHEe6Gc+1Fm9chQuYqkDVFbwhjRzqHriHZAFk3PrqoYOv/tIyRK7bF/ZTi/94imWeBszcsXzxj6Dl0ZVqsjFouWFALWgK2X/MbfOOZXxoGf/LW/wdXlJbfXN3z05x/x8uUrXjx/TvQjWhWKeTbEmOjHAeUzuoc/+s9/h1dffcXV6wv+pb/zP+Dx+9/n7MHjeeh8wr3fphKsqoambWUmr6rEO3AKNgW+k508kXKQxaNgeX5Ovaj4a/5foe+29EMnEO3xGQ+efIh1KxSOZrlieXbG8cMH7BQ0p8fUCzGnbVrHr/3mb/Pg8gmXVz/k2dNPqOuWe2ePefjO91gc3yObokaTkzB8pya+1lTOslgsRCqsXXwDhJ3IucfmHVW4JV99hVsds1qscIuK5t5DTr73K3zy+UuevXjBi9cvcdYU5p6BMhCdyyKOOWHLcL21ZmZcTkHFlZ+JJRueRwZiKv22VMhHugRBgR2dEx+2GPc07pxllvBrepQxYqyRmau6IqfMdheorfROu26NUoq2bfnq6VN88Hz4vQ9om5bValnYiSJMPvqelFIZu5Ds25weo6OCQGEgK5DQzzSZ5rShKVXkdJ7JMn4kULFk9V2/oV22HK1qLi4HUhhh7KiqivrAKEUhwcdZYanqLBXoMHrG7RbjB5avnzOkyHqxJOgWZTX+wVIIMpstdgzSJcwjLaOMW+VENpUYwkYlgT5nlKsQ8XFNVoasnAy0K4tRlkXb4kNgHEYCa1SSeyKkIsU4bNB+IIaRPivGwmg8Pj7CNo6Ll5dMTgwpjUxxMU8JplKoIBq4Bk9jNUetISXY3AS++Pkt7XrkV1eZf/nvLjk5shwvDRkvLOZR8/ixVJKNNkKiUgplLZWuqfWC2I/4IROvR7BK1u0Iu+2Gy2tNdjUZNd+bOWdCmrAk6ZErAjkGrq52bNab/T2YoR9HbIpEMs+ev0ABfhhFBSxGTDWNoWS8HxmUYrfbESP0g6duGxrT4Fw1ryeVJRHXWtM0jVSXSZxgqiKqrYv+6NHx0cw/iTGS+l7izTCA98y+37koFL2xM4jw5Xc73joICitLILapRE3DAKFGp8KmC5GwWePHobB8gjg0qwptpadgtCYboeyP3uO96NANF1ew3tAGz8mpYblQ1KYnjoFuCMQ44P0gLuReE4P8vNK5NMQF0ki5UINjRIWA0+J9NQw9fhwJ/tsrQXX4VwalHVW1YHVyj8EHdrsN/W5NxghUkYoMUNkAa1txiqNdHHF+r0ebmnsPXnPv/kNuri+EVDT2rDUMQ0+Kfg5Ou27g4vUFH//5Tzl78I9Z397w47/2L1C1K2zdSDN8qj5+2YW1Fmf3MOhUbU1sw3muphA8VBaPB20stmk5u/8EP8r5Xp2cUS9WuKolZU1MiWw0pm2oz06IccQeL0mVJZji87dcsUoP0dZJn8ZWHB+dU7crjK2KjnmGiRw1ZyaqELBE4eZNyxU/DAybDduLC9oUObGaZfYssqcl4CrLyekJ7374PdqjhxyfnvNnH/2ZuFPESV1+P9daRrxm1qxS6kCJXqp2Z9288EMhHMwaqFNgQxh3on2qisu7EbgxK3wUclbWRVXljrKK3MuTS0Xw4Q4UnFLCFAKQ9Llgs9nyySc/550n73B6ekpTC8MueOltxhjout0MNy18KwxMs/+dGSEHCXSUi9dgSZoGS4wBrS3KVqhqIfT7AOsh02XPre+4uN5SVQmaDQ/bI9SbGXie5tvEtkgusRYEYPTU2y1VThgNOUpPzyiFvt6gkyIO4hEKilDgSx0C6mDeMqX9daAM7E8zpxmEQKINxtVkZYgJTKjES1OZ0uuLaAxO12Asgx/K2EvA6Qr3hhn3XtQBEZugTPNpSeScTgwDXF/D1aXHxJHt9Ugae0xOtM7ROKidjGDECLtec3IcaerMosqieqUM2bTo7FAYSFtU9Gg1AJqcKSNcgW4YMCUJEHeIqeVR2g0FyRI4N5YZyEMVF4E1M6I9mrYiqRZ9mAldplzTb7KWSlFg01TdXc8T0WbyEzy872d3+bIfGWOEcJYV/a4j9h1htxNJx1waGAcIzPT1VCR87f57i+Otg6BSGaNFWFkcEwJxtyO1NfiITgqGgB/WDP2WIQWyazBOFMEtYMk4MiHLvFPfd3S3W7r1lvDiFY0PPHLw4MxhF0C8YrsdSWOkzohFTRgJ3or5Z0jUrUO5yWFYYJJA6dn4gDIBHzxdv2MYBrz/9iAyJ8HTuINyuNpw4mq0dWy3a549/RyfFKmPUl1MZA6lwFQsjhec3Rdyw/n9x9zc3HB9dcnnn3/M7c0Vl69fkFSGtWboekkOFHTbHRcX19yu/5gQMx9++Rn3759z78n7HFUP4A6Ilue/36yUVGFfSjbm9r3Ag08ugtMKkiwGlPR/nDEY23L+4L3SZ0q4pinBrMKPArWgFXq5oHl4TjYRu1rhK40vXnitW7C0FcujMxZHJxhtqOsFVXMEpppFuA+DoJxv2cBkdMPdWTQA/WbD5vKSmy+fsoyBB5XlUkWOVGSlIrFueHjvjF/5lV/he3rBo8+/5B/8o9/h9etLNpstsiincyf/aiXMSuesTCzmvS2RNYa6EFWkpyIzcyELLDpdBKMlrgUfZRymMHGV1jLwPfegZLg/Har3A8MwFvja0I89gIxuFKeIqqrw3jMMA1prNpstv/8HfwhK4P+mbkgxMgwDIPDldrvBOemxDsMKYxyVref7JqVI0pGkNDlF6jI31zYNu76jTwllK3S1QLcndFko6Ve9ImxGYuy5vr1h0Y5Et+Lk/OEd+y4okHIuakhyuYsgQEb7wGKzoY1F87WKxQBYwzagOk/qR1RjUUZIHCklsCMOYWCmmPYs2CiMc2046H+nAhFbTNWQdcDmjEs1k56vwJoBqx01Bqcst/4SsiflQK0qqjcUmSVBkD6zBA5ZP9FkmQd1me028fS54ulJR83A9nIghB7tEjlUpKDwwbDrM9ud4upacbQCa6FeJLJRoC0+HZfEZkk2hqx3KHoJAAl8EGUePYy0riYrmY2dZhEVwl7WqoSLLG4ZKYu84uGO6ENAJ8WYxPxg4g4YYzF2kihMxFheexIWQK7N6EeqWFHlakYYKESzKSGTxPzQ5m3vU2qsFd3hlNiu14RuR9ztUCEWlZ29upTsY5PwRBbxAp15Y6v7pcdbB0EsRAedjQzaEHSFMZXoezYWkzuIgRhGbO1odcuiXWCaJcY1qODJ445hd8N217PZ9nz2xQUqDujsWZ44crSCXXtP2CbsaNBjQkXJwHMJMko1OG2pTUVVm2JC6jFVjXYVxjWQZTA+JNGs227X+MUpadRvf4KUZCQgld7y+IxmsWKxPGLY9fh+YLvdEIoM1nq7IRaG4Wq5wFnD7bbDNkuefHDGo3c/JKdITiOvXrzg6vKKj/7kT1nfXLO5vUVV14x9x7Zb88Unf8b68hnjsOFXf/Nf4MNf+TE/+vFvUVUNaMNBovWNx2SZNNH1pW8kbL+YhfSgEpiykeSsCFpLtqk1yjZS8acEpggNlJ5CCJHQ9QzjwBgDUWd8Cuy2W1zTYJXBK4NDYJC6lX6AthVZSy8hxlA0R1ORBvt6MDfG4mw1ZyYhBP4f/5f/G+8/vMfDheaeUTy595Cv6gWNE1hl9fgh9x49ZHV+ysn99zm+d4+/+6/9a/wn//Hf589/+lHJ4PcLSKsJBnUyuxT8fH/INJ8SkfQomf84ekYf8WHyjSuXodwnUpVIxjopmGhty94jG5CIE7xxqxVCS7to2G7kvDhXE2NXZvXG+XnGyCZxcXHBl199hdGaRw/uowoLdBg6trstr1694vj4lOVqxdHJSFU1hCTJbE4idZhSGdsJZk6eWlvhtKXPg2h4OhnArmvxFjw9us/V1Utev35GHW/Zvn7FP/j0zxnHHSnu59NSyrx8/oLVakezWKBLlRKNwylJqOtHD3E5YZQSdZ9CxBlGT9ztaF5fsTxdsTxuIWtSilIdxjIQY6xAZEoSEaPBGEUqlYdSBqHVKIypRFpOjyhdFTWaqWZURK3pgC4nrrc93dAz9iMhQD+NWcmdUxI3GQeZ7K9S1kXEHEbv+fIlXK0luJ0uHKd1TUShdOTlnwWOl1uOFx3Hjdw/ddQMN5mbDi5eSKD1ceDiYkvOAaUGlsYTR8/F056ff7XkxbqiXkHSlpQU4yjB2SoZ1BfCrEErsSZLMcz6okIJ2CdjkhhNgU3Ps9WmuL2gVCHAOCGvNS118QbMeS+IPf8pAhqTotPU154EtJ1z85jEtJUpRLDCD57degN+RIVQJACKsH5hKZOL64WalLrKn+8Iib49HGpkcNKHSDeKQPbq9BTXtlRtg8mD3AhGFila2HXEQNaBTJqVOXofGHwkBhnkRWXqWrQtc94PaZosME3KMnsmOId8TxuLrWqMVWidpYdgDNoa0aRUBrJGebn4YRyL2K7hl0fBgpHt2SgowE7jBcpgdcXoBrLS0mfwI7ebrTSerYxDKJWxVU27WLJcHmONBGyr4fjkPrc3N2hVcX11ye31NReXl+w2N9xcPkf5LX2348tPf4ZrWkKMnJw95PjkjNXRmWRShcDxTVdc6/3g+HTMA7MzhFcgpGIFJSidJirJHClEBkkG80z9zzESRj+f0xA8eMO46zArT3aR7HKh52uULn0EpWedxVzElqeB7mlubhY+yxNJ5cCkNSV++rOfsb58zfp8ST6vsWkgKi3KGaPnpKpwdY2rK5pFy1FKvP/++xwfH2OMxR/A4dNiPKza5HTmWaJrauyTMllN8Fsum4UQIlDSVxNtSDXDozHKEPc09zTdRxM0dXhMsNDh+7pzR07Btiz+lBM+BNa3t1xeXcmmjNzbKUWC9+x2HXXTUvta3nO57tLTnCTpUlljUQKxcbxz/xHOVNzuttw/v8fZ6QlHqyVt3QjEbjRWBUweaPLARivGruP68jX94A+CBYSYysYsOruqsLZjRoLOcklEZN28l37r4DO7GAk5cbTdoduKNjVll0Zsv+QCyJ+5s1n+6JlpNXU1BKpUBnQssmuzGaJ8N2exY8vigtD7Hh+8zOmlgikeHjMrMZf/pvt2Ug7KbPvM4OGLV5qbVnG7tCSVUcpwtEucLDTbpWJYJAlaZIYAysBuAB8S3kdevvIoFajsyGkjPfSXLxSvbjWb0eIqWbPC1EyFgTtznNGkuTqaLMfyQUCf7zGkgtZai8yelnM0OTSQKb6cFLUpV0Q5NELQL8bVxapMxMfTTICZ9h0fvIxnhDDDoLJXyf2do7ikxOIOYua3KFyGveXqFAjVvLbmj/OtxI+7x1sGQUVVtRhdMW4GXr6+ZPSRH/zmb3D88Jijeytcf032HmMtyY9kPzDeXBKrhuQaVNswDDvW3S3RG5JS3Ds/RoctOon/mzE1xjQlAAkRYUgDPhaiArqwknQpr2VOSCsJpiYnTE44p4qCQUWOCp1h7HbEbkdK9juVytNJL9smCoNzBlc1ZOD0vpgNez+y6zcMfc/J0ZFsNCgev/s+q6Njjo5OBZRQYhT7+H3IMfLrf/1vsttt2W23vH7xgsvXz/ny53/OJz/9Pa4vXvD8S3Gg+JPf/4f02w3f/9Vf56/85m+zPDqZB8VNfpNrWOALY0lJMH9B8xI5T3Ll030iU9wZSF5YjCSZJRJYMJFCqdKyIvhAGAN+u8NvdoybLcP6ltD3aF1TtUc4VQkD2BqCVoSyCDRCxcgqFU3KQu2fG/emZKPTwDl3rlXKmT/6+cd8/qLl41XFz88aVk4U6W77Hc2m5/Qnmlw5XNuAhrqq+MH3v8+9e/dpFy3+ZlOSxbLYCxvVmGJ+U+Af2TxUeYy5cpO5Q2GDlqRUZvryNOQ/JaRCOMrWYtE4a1EZbEpCGT+E15SiqWuMNvRdT84ibReLDKDWmsViQYxRDHSjL4gDvL54TUyxiJTrIopuUWipYgZPbCd2bCbniPejwIZaz8E1FnyrqVv+7r/0r7Lebtl0HR8+eYej5YLTxYLaWaxRwpB95yEp/ogXX3zEZn3D9fU1v//nP+eTz77aZ/YKqmYJyrG57bDaCizZaAIwasfHR2eYcp7DuCGEyDgk1iRCTjy8vCa1jqPThTBHlbQ6dJacOBstDjIxUFlJNqYe+LR+JTSKY0jWaUZShCBkUGlApQH0ljH2bLue3XAtc5RGBv4L82i+IUXko7h7TL8IEX1ISTHkkkzpxKcvVJH0qwjRYnTmgzPNvZXjbGlZuh2qJCNjUowRrjYwjDB4uNpkGqs4aRxPjiM5KZ69bnk1NtyGmnplxQIqRbSKaBIUKcKckZYHeQ5gOR+ensPenqJyDltNHo6y76aCgkw6vjkrjLY4V8tMqzGEyYGmWCMJTBxm2D/nLKMbMTB2I75IpU3rr65qGbtQYpQcvahF2ZxKEyjPa0WrPZqXtSJnITjt416er8rbHG9vquuFibVyLQ8fPCBqw9n9cxYr8cXbjRG/6+hvb+mix2ehzepoUTpQhYTTFaere8SoSD4SVMe46Yl9MVKcCQMy4xJ8KH5eGetqJj1Ea6XiczZTxPUxMYMfRdInJWxV4doWa2swhpgr1j4yDtM81S8/5qqkRMG8jxxAUabRMng8DD2QqSrL6ekxNzdrxjGwWCyFXeqqg6qtvLJRNIsltq5ZHB1zfHLKe++/xw9++H0+/P77vHj6Ob//D/8+lxevubq84h//zn/C86df8frFc/7qv/A3OTm/z/LkHNTdWxmQHkz5M6mXiEyaQHgxlZ5cSJLw5ckVWu3JG6rM45QXjyERfMnygicT0UZRNTVZaYahY+g6VFWj2xpUQiYrEzkpYvKkJEPfmaL0O1WlJYBQmJky6D8RePafKyvo/cjzqx1jb2id4aiqMeNIlUfeXW95MAxUBqLv6Pue9XpLSICpCXlHzhGVhRThjKJ2tmS7ZdA2i0+Z0WUBakTrNmeSzxye7Klqm4PJDD0L9BlzKnN6IuxQaSv9o8N7MGcGP5AQxp0tRJxxHAglox7H4vFHBISCWTeNDJ9vt9ze3GC1pT1u0dpxbluq5pi6qXGVk1GHkIoF0wZjNG35eZTGUqDzmFnVC5b1Em0d987OqZ3FqYgiknOcK0mxwyo6wASWlWN1MGJhjOVf/Fu/zbAb+OiP/5TNpifGATMkTGEG79I4l9tZFZNhLKlqUTqzGXq22zXd2tFYA1ahavEwTTETxwh+QCVPNPJedGa2sAoxkJTwBSQ8iO6oyJtlIeEoD3ogu4j3CWsylVWEUl24ykrSMsXALOdSq8TkrQlKem45kFVJLFWGGOmGPIu7S9IOY1TcdJExilOHD5luECanCGIrQlKEmOm94QhFozJdLmu7bTFZxl6CV3uNUIToliJlvhKi2mvbSjt2InUxm1jLvQzL5QJjDWNKdKNYJcVUIEilZ8GEGBOVEwEOa82dqm5CUSZ0YxJ+kHELz67b0XWiD+qco0kycjdxGEQUQsGkJkYWoZIijzcDnhnuVLKH6N0//Z5gLmiAYlHVxBMLVRFLtYYUE10/MnQD3XZHTyKoTLYaCziAKMONlTF4pLGalFRDwUfRENUBTEAhQ8IxlOxDiTiuKuLKzhphm046wZT7M6YiuZbIsSiHWBneHRKs+5GuC9+aJaQ7sMf+670rc7npdZ7hlpQSwzDIxXOWxaJlvd6KsHElQseHKijToZT0m2wlA6tqdQQpkOJD2kXN6fk9Xj5/Sj8G1ustX3z6MX23I449Dx88IKdI0y7Jjjuwp7w2dzZnIZvs/z/lWGaFlQTAyUORhEqQciiN76KqkfPskzdpB2YyymhsI3CbT4ExjOhxwAVfpJA0WU0uBEAOqCK/t8fwc+nPFeuXPMkpfR3VkPeY8TmzHgNDzCQa1JBwKQhUHwReSmFk6HZcXV3S96N4IE62N8jvE+FlXaBhOU9yPwm4qHVGSLlKzlfRBT2Eoadzmsv1jRNsjMziaZ2pjJCflBK90fAGvDaxDUOMshEgPdDJn0/krWQjyCmhjGivjuMoGpCjsEKtsYUJqXD1Uq65hpTULFDcdbu91JV1co2AFEUAnJyx1tG2C9pmgbMancZSEeQCX5fVoS1oAzlSWVtMV8s51orv/+BDNte3fPXxR2y3xZVgUKUvnenHYZa4Uw6U1uI+XzmMgcEPMqfX72iaGmUsWonWLUkcZ3IMqOhJUc8938nYNcYo4zhZ4OqS1sl+JmVS6Rh6MJQ9RWTOQJjmM7T6tetVfDaVkK1KZ7EM75sZLg1R/jUKnJL7bAjSP+w87PpMP2Y2u0wo91VVTaL3kLKmrgCtGXIZBncV1mZskPnLnCnEl7JGUipEEjnPOVMUjMrtm2RsJ72xH7VtLQiT9wzeo5QEQCHZ6NlhI6epZ2/RRhf2r9w7035sijH3nrW5lyGcSDdyDjVBS9Jb183MJhbwovQBp313qgDnG3Bahuqti5s3j7eGQ601LJYND57c516hHLt+w/paxIU3N7fkGHFZwXKBrSxq1dIYR60Mpo/4zY717S03t0KOeXWxY/Q9OXpO24rGOhrb45wogefJDFbrEgQLzdfu2wFKQqywDQvZIg+B0HvibiAETVSGm9uBZ+oF2+24N1YtR84Z7/2MWR8yjw6PqbQ3RmNd0cMjM/QbLl+/oHKOupJsRgZIPfYgAP7CI+d5A0VpsA3vfP/XuP/kA07vP+T3/uF/xp/90R/wR7/7D3j2xae8/Pxjbl+/4IPv/4h/6b/73+PR+98nDt0bAZZ9T2DC+QsVOSWPLwQQrRTZOTnP0h4RSKIEQZ2V7PspMw6j2J6Mnj6MaGuxJydU5liIS4NnSJ7Qb9CdIyaLqwzGlPGC4MEWGyQ7eaBJcqEKdVD6CemNZOTghg2eR/ce8JMf/xUqI2LTv/9P/ggdPK3JuHYpzfoMt5s1L776kr/3H/4H/PTPP+Xq8vJutYdUa1LdFPhTixC3yRmrA9ZqbO1kPnUUUQaKyaoM0ydyEteUqSqMxb0+F/iRpPBR4VXG6MygNZ0P+xRLQdsKtGSszDLmlMmjQKFGqZkpKooaoIxCOcu9s1POTk54cP8eq+USrWRWdnO75ueffMzx8THLxYJHDx8RQ6bvR148f4XWitVq8/9v782eJMmu887f3dxjybWWrt4BEASaGwhKojQcs7F5mJcZ018xL9L/OGN6GZOZJKM2moYgATaIbnSju9bcIiN8ues8nOsekdULq8mRmSTkNSurqsysqPDw6/ec853vfB8PHz5ivV7TtksGP3Cz7fiLX/w1Oz8w5syf/sFPeev8Ae89PGMyj9Z5SggVplmgTMMYCtpqXLPf60opnjw64nwJF++fcrKAXe+53kVGnwkBKCt01nXESkaA8jBg14HGCfybek8ZB6wr2OQgGnRJKApLq6ulkFgVkYCQ8T7VsRjpN09935S0KK6ETPIjKuxY0eNURKsl0yT28TrTj5Hr3cjVtmecK3E5e1MuqDRVPFK5NE5OokImm6lotMxqRUqRSficeXUrNl+HR5GijpGUMn9dK4XTieNW8/hkwc3OUIrhqD3lJAe0ijzf3KC0mk2k53MsS6Exx/EakOfrqH35w/t1enomEHLXUZRUfCnWZCILamSNrQ4ylsY6DOCLQiWIPkgVr6RoEXawomnEgLjrOvzgGQdBOXo1sL3tsLZh0bZQFEtncUbjTFWKzRW6nzLP19okX3ee/lfoCRaGsacbdnRjVyu3wnBd8GlkDMOME7euISlNSVBuhS24bFvaVtOHzMhA2F4z3g50twlfFcNPF6r2Xqb+n6p01wnn1we97srqmrKCoqr2vNz17BNFJYgJsx7QTcvJUcPSLWga+xVSQs4Z7/2svnG4DskTcxCehYQz0Y+MQ8/Q7zg7f0C7aEWSiUoJNl+lA8snyp0bNT0kc39BaWyz5NGT9/noD3/K0dEpKidePf+Cl19+xounv8H7kWax5kc/uSUpd2dD75tTQm6ZjDKlyhLmFkiikbTgCEpP7C0tkKFEUCZCzOTsEFIkU9DWVuUc6XU1xhPR5JJEWFnn2sM1s1qFUp5SxEYrTZJiqdSERotYc8o1MN69T1opfvrHf8jv/s73+ZOf/jFj3/Hq1Uv+6q/+EhS4xvDg4QOOjo9QGXTK2JI5agytzrgSBBadRjKqEHaMUeyQoFZSGmUtEwISYyKVQPSZSSpbqb0n4GHiNP0919ef7mfleNFXqHU4PP0KxJjROtX5LclyJ+p4LnuCQarjKUY7louW8/NTnjx6zMOHD9HaiVpIDsTgSdGT4kiMIhAeo1RKIYpeqCRdQlgqShNyofeeL69esuk6hhR478m7WKt59/xYnq+avU8EiFKz9taJwsrQd9PuRinFerlkRebR8QKXA93gOFolep8ZY6HzmhQhBxiGIChDyIRqkZaTJWVDURbtDMpWv7uJAdk4ggcVpDoqShOCoEuxQuqq9n610RQPo090fST5gAkDi5VFNQ1gMNHSLDTWKJT2dGPGj6IdfPisCiP97p8LzDZYMdcxGWMOnnlBCTSCJE9qLtNeNyLoW/luuf4u98nZwsmRovOJfii8uBoIKeGrjRg1oau36OBQuRsTJrcGSrnbO633a7Vei7GutTSjJ4ZqdDsJ79c5SKM1VosCk1OaVlvxQ6yGhqpU5mZFeVx1LpnGg9LEeM5yjU0jcOpxCDIaQu3/1p77JJIO+3Ph6yq/v081+B1MdXdsd4ZNv2W87YiDR/uBYgQuOjo5x1lL264IJVS39w3mJNGawvHRCh0yWzUSdzeMNz3DrsErkTGaPO7kSpj7LtUetEJ5NQUoBYU8JHtmnqGUKOwxL1BdUdD4EecMZ6dHHK+P6HqRUzr80KYgOI6jyCEdCLJOGeSkkC7u19VEtRTGoWMYdgz9jtV777NoFwxDh1JCATevzcRMawp6+zXV9nqPttmGs4dvs1yu+eCDH6BK4m9//pcMu1suXjzl6uKC66sNMcPq7NHdAF4OGYCiZSlVSxJqfNVQzUqhqwS+SoqsJvpxfUjKfsQiRKkgY4oUVVDOYZtGZJRKkQRoHOvQ9ojWucIpzb7/gKcY0VxMOc/mn7pIX3Iam5ie2sNPSBvDn/2zP+WPf/KH/ON/9Ce8evmcTz/9Ff/3/2Uo0bJuLY8eP+Lk+ARVwORMqxSPTlcct5qFTgwTXFmq0kvK1WVCRNXmIKh1JRSIcohKihSyiEWoPdtt2kOH+2k/klLmfUipepxVaHSsQ8nTClUiLcaE2KRKEuV9nD/3UlmKyhq0c7hly/n5OU/eesyjhw/pxsDVzY7gI8GPlBxFfzQFcg6khDgO5Fx1H5Fel5YelI+J3eh5evmKq90tXfB87/IFq0VDLu+gkcCeigzZSxIjwbxpWsZxYLvb3knC1+0CozKPjxccmcQYIudesQuZIWZuxsLoC+NYuLwc8UOqBsIyi6lxpORIWEzj0NaSa2VnrcKtWpRVqKCAlpQVIUjFHEKcB8WNEfJEKjD4yK6PpDHQhIF8dIZulyjV4mhZKodOFm0Gdn0i9j3Ew6HyCV2R9oLIiO37wSllfMw4Z2mNqeeNQOMzo1Sr2lKR/SgQoK4iC/L1UiHBkjPWwtFRpNkktjnx9GKH0kIyE/mwIg7rqYKDutSTc9qTEnRDZH4PjTN3ni+lFOv1WpwhmkbcU3zAqF0VyHain1ufT6vFzLhRmqQtwcA4KS2BzHtWFaOmil6UXChR2lZxDDJulTJxIXs+hEC2EhOMllaNFvLFvhKsB8PhMzg9e7mOqH2X9cZB8HZ7y3atZQbGJ/Ex63vahWO5bCAmQurY9B1j3xPHkW5zw/a45fJ0wXvf/wA/BoasuImWm2QZEqwaxcpq2tqLCRRiCrJHMhRitYVp5ypYp2lwH7KN1MYc4zAwdlvGbodtGtr1msEH9CJxfHLMg/MHLMZ8R1dOKZnPOj4+Zr1ezx/q11UhWu8tW1BCRb+8eMXYdxyvVxwfHaGU4cWLZygFq+USM8/F5TnmfbO46zQvM3WmZL/Zds3xwwX/8//2z/nxH/0JP/nTP+Pf/T//ihfPnvL8xSvs8oj12cO77/mgjTEdyAL5HjC96v6J3pONzGBJv04q3pSmQ142WKzOASUnrLPYtsG2LVMWaozCpCgZfCWEFC+voypzMSU55GJITAPyShuZ31J7J+t6E+48pMYY/vd//n/w4x//iMVywcn5CQ8en/Mv/uX/SRx36BL56Pd+zOnJGX63JXQ7Vibxv/7Tn3DUaN5/dMqf/6efsRszY6QyeOPMAs1Ttqnk/qacMLoQO4XRGdIklVYOsuy7EPo0IzWTYySVlcCjqi6lseS98RMAo/doI4Pvqd4npRVN20jvL3hSzoScsKsldtminAUj8OvLiwty1kSf6bYbNjeXvHz5kgfnDzg5Pib4Dm3WNI3j/OG5wGyuhRjobq95+uI5nzx7xW9eXXHZ7+hCwKdM5wN98ITocdrIc6k1OWVSyLx4dcFmc8P15TW/eXHBi8ur+Zoohe7iFUsVWDpNaRTOao5XDZN5cl/k4Oxj4XK7YNsHXl4ObDY9w+DBR0aVuRk9b+V2tolLyhC1xZoW6yQo5mIpIZOCMGBFwF7hgyP6BtdndkOmH7MgTECOmWZ5yunjJ/zgd3+vjnR4bq+u2W2uePDsM24/+ZTLly/57PKCiXcYfAAKziqpsEshjLnGByXBOmdS6jk+cjWpMiQKoWSG0WC0xTSwWE8ol8KqXE0Hpo9R2Lg3Y+E//rLQDzB46G2Y5RSLnVicoPcWfIDMMUpvVJGSiHNrrXCNqsjOXhNVfCVPaduWlfdc3WxYLuDdt98Tq6/G0Syaeef63Y4SImr0tNpitUCurVI0WnN0dlbPDeEHxBhZOUder2m0pm9aBj/S9YOQmPworFEnSbtVZX+OTaNVtRd4SJibgt/0zOlZv/fN1pub6o4j4zBSQqTkCCWJAWqSByKHQKEQcyL0PWH0+MGTVcaXxNFNjw+JzTbycpu43GVue9FsPGoVptRZnUqEKfW6xQ0+i7AwwiDUyCC89NGQqqQIcy0XUeyPtaIJQXqEgxdLmrLfIXdu/uGc1uHXc62eYgxMFGFhKBVSDNxuNpAT7WIhh1sphNHL4eXEimiSu5rumtZ6vmF7dqGS7E3dnSEryMwf2rA+OZcmtbVcXrzi5OFbmOWnPHzyLkdnD+4EwddnzaaNssdGJtampBa56Ln6KKZ+vtVup+IyTFV4oUgz3MgBDwJlKgXK6Kqqf7B98lT5ymvkCouUIvfDTpBczVgPr+GwWlbA8dk5Ryen5JJol0tOyhkfffQRYdxBGjk+PaFpHDmN5NBjsuf8aMn7bz0g9B0ff/wJhZ6QIrFej6hVCNs3pVTtSwtKJUqi9sAiJZYZqr0DuxwEwq+4xk9fm1ndGqvs/LkdXtsEu0cOqknqAVcyRSt00+CO17hFi20afErc9j3PX12isMSsePnyKZdXlzy/eMXjmye4puHoeIlPoPXI6D1KKWH6ZoG3P332jC+vt7y43dF7j0+JmKUai1kSGlMJDns4Cm67gYubDb95/pLrrmeIab6HpRQ2l6/ITu66MZMSkK1VgqIBxgxtEum0o4WltY7rZUPXB8be066MtFi0IavJlkqhshyWKCPVOftnaZqHjCkzek9QA0ENeB/IxYg3YXUhsIhb+dGiBWXFsDZkSo70Jye4J09k3vXTvz3Yn9PeoSaWk1i6kBXkWqcDvDqUaFNxUOkRT6QlY9W8HWaVl+kMLNIq8FlxMyh8NIRSKGYmV8/nREGhbUXMalDMeZIKFIg+7S1PasV5ZxvO4ypAle1TNG07k/9QZa6qo0ALmJJn8ospiRIjcRhotCSxBsQCKUYssHAWXVoMyFmaM2OItfKT1ywIcxclQiVlIspxlzF+mITOfoTfrRB880pw2Pb0C4tKkZI8WgU0kZIgjRDq4G0KgTCKRmfMmm4biLuAajb4kLm+7vjZFwMvNyM+gnOOs7VC54BOiCqMFZC9aA215E9prFFRo9wCpbVohdYPgrxnK5rViqgtQ9aELtLmgcWrG5rFqgbVr+LG4kp92KObyigZ8N9srgjBVwaepuRIDCOvnj3j+PiIB+++J41pL9e/Wq5EBV1JT8l7P0NHQn/3M23YWluV1KtRrNXzuxBIsgZD23By/pjTB485f/AWl5cX/PznP+f3/+gnxFptHV7PBOOm2ruZ2WxMMEsRBYlUA07JKNVIvwiRxgsh7Puhujp3UzBOeizzQ1S/b5xlatbq2hPdF0718MylQlVS+Roj/bdCmTPiKvov7/fg/8AsyLoh+g5tHavjU37y058Sx44cenSMlBjFFHjYQBg4aRQ/fPcRJ63hF7/4W1BX3A438l5KIaaICC/LwHYoBZ8zOGitwqHwYyAF0UZMJc8i2/L+BMYt9dr220gSiTTB1MqQ0CjtROPxYO3trmROaupPyzxlwkePWa9oT09YPXlM0za0wGYY6L2neLH/UdryV7/4C66ur7i4uES7hpvbLb/zgx+w6y/YdT3r5eTTJq2M627Lv/6L/0SwDdE4tmFkStlSycTaLjCTDFwdNs/AxWbHJ09f8B9/9jN0yfgDRD7nxOe/+gUPj5eY4kVoQmvaZm/f0yhFkwttgpNG/CXLk4btWOh85unNCHFA5YFil2StBGGIMve2TApVNNRZWdk7eoa7vR/ZpWuGLsGQKJWvbu2CHEcKmSYOLP2Ohe9wq1N0e0TfXBAWDntywoMHj3BPLlD//t9WqK/gtIxIpDipBCHBz8pzsWjFaNcoUGlAFdHPLbSkAmPycz9RXkug8mk+NmZmyTltFbkYxmjxxYjkYiu9ZTKEnKGOikklWCApQieEL2G8iglxmrTrjEY7MIccwQLZRxKKcRzEkkuLNdPFq1fstjtcY2gXLYtFCynjUKy1wVVY2MZC7Hdsxw4VjiVgGoMfxJHeUbCNBWcZm4Z+cDit2NW+oyFTYiBnma3URpLrBPWZE2b7FP1fD4DTWfVdAuEbBcFC4eZ6w1IlXCm0JmFUZr1shbRSWUBUimyOgRwiOkVKiMSUufziOTFm+j5gfM+iiPP8eqFYLizGKaY6W2snQrfF0HcjKQZUDjRW+nKLxQplxIuu1KHuMEpJnSjsQqYLkRtfME3DOmkejpFdN9SK4+71TT3B6QO0szGkfK/bbXn6xRfsuq3cSKMJY08YRzabG/q+YwyJq+sbUkpcXV3gWkezWFTbEI1rHCWbg2Ck5tcXpYypFzmBDXsoUHoD0+EqSvzLo1MeNy3NcsXp+TkXry7u3PhJp2+C5mCqNKTSLCGR1X7mS2uFcwZhSlZrmCJsMG0kEIuJpQTS2QG+RriJ0iMDr7Zed9Vy1JqCmgfjU73mPa6fKHkfYOubraK7XyeBpGYWn1IK2yxQJZHJAu3Gkei3hGFD6DtCtyONI04V/uj3P8Iuv2QbPuP5jQxnZwq50ryNtXLI1swzpczYe4ZuIIWMisKWTVPQ5qA3iNC5p3kpKJydnvLw4QOuNhu6wXOz8XT9uA+Mdc0jNsYAatZYNNlQbGF5dkRpW8pqKZJiKYM1tJXwEa3h+dULvnzxlOvNNcPQs82B//dXH/PLLz/nr3/za2KRAfTHZw9RiKP6NgwMKdJpjWkcxjlS9JJ0GQPaVtHpfQWcUubpi2d88umv+c//5S94efGSoeso0eOH4fDgoN919FbTWIOzUi1kNZknyx6zWaTktJVxi6IdTQvHGRYLS+gLaQhYEjkpxiBkIWMytonoeQKwwtFRHOJzzqg4cMwVazpULiTVkmjo6RiVZ6cdQTeMytB314RxC2hunz/jZjdwedOxePKQUquiaTWNPBcpisIVKELJQgYzBWc0ViusUlDEF9THEVSmoGlFMZzsE0NGULBppKPUhHZqzUgkBRXIqgqFHAy+i/SbfOBTJyFn8HUUF20orqCNoTVCZNJW+ADxYOi1lMLm6grjLNswsjo9JcbIF8+e0d1uCd6zqr5/Sisaa8UjM0ectqKkFUU5TBUJhqlWiIK2TKo+tczVgDPkRYOIjGlWjcUpmbnUTK0h6Y9S5ISYFGKm8xO+rcX0d683rgTHwdP3llRAtwVlC40xlTJOZSbu8dpSZBh2Gjwed530ekJirTO5gWI1q0bhnIKJbIJC4WqjXtEN0j9qtczZFFurRTXvAEolNwjRQvy0hpjZDQGbFUpbhjHQDXKDXq8Ep37ZVD3NHnYHfZ8YZXA/xkD0iW67YdjtxC0DROg2eKAw+oFxHBnHEe99lRayM7wx3bQ9THpY1stlzQgk9c/lbiPYNQ3GWVzbSgVhrjl8SKdrmX5NgXdS1NBaoVPdYJT5nglhRs8bdYJSSskVEp5eW9X3VQ7e6xTwCiVO7MtUFR4qu7Ea2k5O6WpinzIReSb4Rn9jNifVl973CtS+i5qrfm30A3HsiGPH2HfEINn2u+++zXUf+OzFNZe3Hamk+XMrqJkQI9ciWayzliEz0+1TKUy0llmcqsLFAhWDUYbVsuWdtx/zve99yGdfPOXicsP19UtCFFLJAfAwS8jJvdtfj8i5Kex6RbSGkYKaDIG1RjkZW1DOESn044BpW1pjyMqSQ6L3kevtjqylNOlTQlf1n83giRSWRyeYppH5w0GGpGXQVlOUYvJFnJ6Vy6srPvns1zx9/iWb7S0pChkuHox+FMT1w/sWRXXiQHr9qo6CUGIVa5BqmBokrVU0df95LKEYdMnEIgpEPmZULCx9lD4aCaPKLDieK+lKelSh6or2ZJ2IRIwZMTYSrBHR/ZzxfhCIPGW67ZbdbmS39fhzSVLvIEVK5vKyQpAhautkGsSTaXWESVKf9ZxQKqFUwSlTfS2lLynKTIkYK8qg9qiIygCinWyMzLZOQtESEPVcoZLqvO/0NkpN2OQHsUXNr5vrnj88B4ddh24sQ/C0R8fEnNjudiLWcNjvri0RkMSjVC0zCVw1OYz7vTBVZxMipJA2SNaKhRMkSClNU2fLTZnQqdoymh987rR5JJE6DIKlzj9//dnxdeuNg6DPMPjC4BPjGGlMYb1sMMqg1STYqmSQVVcWkc0sXEOrDTlGwcWV5o+Wx8SiyLbhuFUsbSArK5JP1WNwHBNfXm0Zhx5F4QfvvUXbWlxj8YAuGadAxUCuSjCpFFKR9yCErEhfHYlvLpdYUi2r72bhUyUoNjWqymgZFArrHGfnD/n95RLIxOh58eJLPv/0E243GzIQUqIfB8YwYo2mbRs22w29Dyi35uT0lJOTszsV2eSkfNiYPsxmDkv8w7Xv9WU0YvOjtUG9ZtM6uStPFjz7IFirJ23BFnJ2pOQpJRPCQAiy15x11XxTE30ghMButxP2mG3nvuhUMalJrLfymkuO+DESR81yuaTkPDP2UpL3Y5zDagfUAKAFUpShXF01Yr+2DsQqUEVMNsNuSx53JN8TultCt2XYXIoh6jDQdz2+GIpu+OGPfgjtMb44Xr28oYyZrCWTz6g66F5QJXN8dMKD02Pef/ctfv35F9zc3HJ1syXlSMhZdCCRWcopYdBWcXJyzIOzU/7sn/0T/vAP/oCf/uSP+df/5j/wl3/1MZ8/+1fEkvETVX16ECscCmCt3LNuu6VdLmlcS3u8Zuw6Ll5c8MGHP2C9WrGwiqPlEavFiocnjzg/PuNH3/8h1q3IBYKPWNNitBUmoNEoa1i4VqrcnBlinIetb29vue22rPQXXPc7LvqOogSVEd0OeVYuLq/4m49/yb/983/Hze1VlVyTYGzs3RncIezY9YrBN7Nd1XK1ZqUsrdby/oyYJEsVXKqaUcbkDDHQtIpIKzZRUXpdmz4RiwLTI9zRiEMsf2LKDMMg5r/NkuN1y2rRcLwsKDVSSs+myYy+sHEOiyd0OzZb8V8sBV52mctt5NV1z4l9iq/w+bTGcT9moqtsVSbhBwlkPQFrLU3jaKypIu1WeAX17CpaU5SlFEmelYMOTUzQJ1VJSDJ/N621FaZoOzll1HsirY0pUanJWREtVD8yWxk10xFTeXElHghol8Lu9hZtDbsYWJ6cULTBugZnxGFl3TYsVguWyyXr1VIIkWFbjc0nAQp10PKuSjWzUDlzhWu0ptUa4xytlT65sxY79SV1JEdRDitKeoepWn/JyAaVu8A8Ax3T3kz4TdcbD8sfH59wdLxid7Ul1Ew+l4BRUQYntVy4NaKvWQwymGksaPGo0yWicmDp5Agv1tIoyeC0krEHraQ/41xhtbI0tkUraFuDayzWWbLI4Au7aberrtdGvO5KQWtH6wzHS02vwGrNOAy8fOXpffjaWcDD3lmIYf6wJ3zBNS1aK2xynJye8+itTvQ5naNpW5arozo+oXHOUoqMfazXa6xxM2tQMp69mGyc5IbYV4mvr6//+oR7T/Dpa999LQjOTePD71dYKoRS5eYik81QMkEqIW0Y/Tj3B3NaQJF+IQi1XlVWGEoGh6esNEyi5TnM0GKMFe5RGl3k0JO/l/nr81xm3eyvfRqI4LH0/VIY8N0NxXdk3xP7HaHb4Xc9tzei5dqNAdWs0I1m13tiygI16tqD1bXPwsTuFKLDarXk/Xff5n/5n/4xP/7+B2w2t/zm6XNutju2fUcfRqkOY5J5qcZx8uCM999/n7efPOEf/clPefvtJzx4cM5Hv/s7DL3n/OyYq9ueMdyl3DdNg9GafuhRqq1ZviYpGElsri7wY4AxCNlAW1Z2zXtPfsDDB094ePZY9HKtCBuLlzmIFLLsFSG6ZOLoyUl62uw6fAiMwVN8xIyeD1ZrHi+XvEvhdLWuvawqq1cyfhxonOXxgwes12tQCucaYgzsbrc8/dXnTDR8RUIRyKlAsZRiUKPMzuVcxLvQGuwkMF5Rhqkqbxd61sENV5dy7oRCjgL9DUNHLAlDpKnwO0UAgqwtoV3S27a6TRQsGasTy0bjTKG1UHRC0ZNuL0gVSYpDh0qBlQ0i8Zb9nfs1MYpFDSlIUKlomJxlgjCkpEha9jXF1GIBrJbqKSPVbcFKRaMSVhckNaxjNbWFoRUTr6aOtkjiZasUWjYSBMiIclaRAJRTrdpUqeezPEbOWWyurLWJaJKS/D0ldBYnlCePHtc2g3zGjbU0WoudFZXTPvUvy9QaKfNpMwleT4SWuRos8jnaOv6gUPXf119aRDWc0lDd6FVOtTIssyj9fKbODillbs+8yXrjSvDo+IT10YrdJhCjpySIQazujVJV1UXRGEXbWLR2ZFPI2lG0pVk26BIh97S5QgTGYtKILuJHOG0eZRROwWrpKI2qQVCyRelTGHmIY2YcelKIsDgR9lPRaOtoKBwtNGL/nAnec7vt2Oz6yojcr6kPOMEoMcY526BQ+2XifqytZX18ysMYWVTyy2K5ZLU+qYwwmbuLUaThKE4ehihsWtizQyfrkUOG0wSTHkKfX+8ooGaIdL9t7v7QFNznoeiKtapatkxBphRJanwQLzooJGWYnNL7vpsryWn2UNi6ukLhVKijsuMqyBLDyND3lCIQ6xQIgWqWm+b/j6lfoKYAvYd096s+ciVBCSQ/kMYdob+hhB7CQOh3+L5j3PVsrrf0/UCfEs3a4VRLuN3S9aPMj02vX6FdUf8XIkspsGhb3nr8iH/ykz/g9sN32G63fPLrz3lxecXlzQ033U6Efr3HKMViueSdDz7gRz/+iPfef58ff/R7oiBkLd//8AOur285PzthO3hKN123LGctKEXwfu+3pjWxzlldXt+gEjRJzwfAwqx58uh7vP/+D1itTue9aI1U0sZaqOouMSWGEBi95+bqkuh7ot6x20V8ykSfyWNAB887RytoHKlpYbHGWVfhNak0Yggs25a3H7/FEIWSvlyuGMaeq8uLGfYEOdQV9UDOApF7r+cgCGKiWqpcmWEiQ1TIbKExVqTkbq8uZN/FOhSewQ+RVHuCWQvb0BoFSlOsJqoTRtdQjMHiaZHemnUygL5qVE16RtL2klB7acVHbMqsbMGWjMrhzv3SVUJMqpo6jwgYPQ2KA4jY9dwAKzI7J+dloSj5dxlXUQiLVqJZ6zSEDHF6xrUM04sojszz6vrYGy17V+lMqpZGe7cTYbBqXdBFgmBNizhaOlwpBzGwwrgJVJIZWqcNJyenDP2O4D3EUeT2UJjavjEAsxD+1BmY2hrULllh/819cFTUhGJK0nP9IaUquoD0NCcoPca5t6jV4flXE4TylZPw71xvFAQVMvO2WC6EOm4bsVUKfR2ZSOicsFpLWZuLSOrolqsusBlGlr1n1WrOVg3GmfqgKLReoCkYNEYVTC1mrVacrjQULeQK5aEESAZdzkjZMhSHL9Jrs86RsojOFjRFJRoNqwYRDw4B3wfGPnylstJas1gsaNuWSe1j+iTVfIMUJQvUul6fs1yeiobj1L85DFQqI21FhcqGcRwZhgEU86D+BH1NprGHAU+MUffvbRrf+KrFzkEK99qaiDFTMJ3GPeqVoJRkqeQ4Q6Sqip7nFAlFRjOssVXHUcxllVIEP5JTwWiHNW4uRguF6GUGMflAjpGSA7ttzaKVEojVWERWSYLaPGdXE7vDuH7ncgsQbynjNWnYEbtb4thRxmvy2JHGnu7mlpuLa55//oyf/+JjbruOaBuUbcnactN5NruBy03HbbclIVWE0+JgEYKIiCc0Xddxc/mSLz/9mLNVw5O14/v/9PdlRg3FmArBj4Ruh6LgmpaHT95heXRKs1iSwsgYA6A4XhjeenDEDz98j6ubG66u71aCw+hluLoV09ucBYAcosfnghoTq2bJyckZ6rYjeMXuZE0Knpw9F5fPWazOWCWDs0aCgRUWXW1XMvQDfb/j6bOnpDig8Tx/9UL6iDqxdJFT5zhrDdbJr62ztNowBFiGiDKFtrF8+O47nB2veXm5I6NpF0dc31zWPvN+WTJOC80/q1o54fFRlG1C9BjrsE3LYrmo+7zQtBbrDKt2SdMo2kVhe/2KXAp+3HGsIpFEiRllHcpYshUBbZyhbTRWN7SLh8Qszg6X/Q6VIqYELB5TJhi1YFTBIgG7KYl3rCfozGATZE0XuvlRU0pxftTQOEkw06QSUyqZowbwXCBlkXmUACPjGRotAa1k9KSzjAZlUdmiS6ZVkrzLcSIVFgViViSlyKm2oLTC6UlvtzBGRYoKPyJGBUWSctcoVgvNo7M168WCs/UxJ+sVV9db/vzPP6bUxFWTaSohatxupY2lhOjiSiHUWWHvPWp06JLB+9qE3GuRqprEKtTdY6pQCVaZovJemKIGY20kOco1EE5yj5NbhdZGiFs58frZV2qAnea533S9cSUoZXihkMQvUBkIUo1MhoZTZSOebaIXl3MkxsKw87jiUAvHJCclSjE1G1fCDkyl4GMluKBlYLsUkhJijFYJo0bpKZaCsgswGWwrGVgWSEsEkSPOSJWqMAwZSa9eWxP8Jh+kqDfIN2q2Mp3KtQKbaNiyM+dXmf/9JCs1wQPC9qo9tLJXEzmUY3v9ph1Wh1OFeDiqsP/5r7nZNUNSWqHLVE0dNK4FaxAoUympGpSuc0yVo5nLLPu2aBdzfyOlif2IvObcwK99iJrzmSrwXNjLyIGuM0Zmr7ahkJS+anEqYyoUWF//8DOhkMYdadyRfSe/QkccB7IfSaNnu9lwdXHJs2fPuby6oRsHsmtJdMSsRETdJ/wQiTmRSsKUqtZxAMugYBgGri4v+eRXv+TB0YLVouHs7JR2fUSzXLNYrGk1JCXVqXMNq8bRGoUmE/wgD6ZSWOtorebxw3OWbcPrrc6U0uwCPw3GJ8pcgbUYGutwbUuOBdUozs9OOFpYFjqw3V5S4kCOgXa5EpPgpmHKJmQsZ2QcB7wfycljVECTcCrhVGbhNI12KCv3McYgM2A5M4aAD6KX2feV7m6lZ6zqjGmeIf/De1azfUVVOJGqT+s6h1mC9IiDJEFTECw05OywNleZLkWzPCYVjQ+FlEWVCetw7RLbLtC2ojXG4BIUbdGrBTEbQobbkhBT64acTD2bAh7ZA808awhZmfoc1Qr2tRaKNRonOOQ8tO5Kxujaz1KKipRPc95CcNIyP6hFnUGqGyU9LEEsRSUlo9FFfBOn6lKlQq4P20R2UQqiPmivJLFBS1WhCAXOGdarhvPjhicPTlktl5ys1qzaluDTnSA1VVhOa1ROlBiIQ1+f00Kp2rVZKwlUpaBznhW+SoVXp76kuGzss9npv5pPr+m8rWjVnUe+7Gk7UmVLC02TyVlV9as6Wz7/nLBRv4tqzJtbKSGsqlxHG4yx+JkNLZclATEz+kDOYJwhZ01Jiq7raHNBrZcy80fBNKLZp7StqhqKlBI7X2+iNqInmBNtFjzdqUyTNkLdbhr08ljGHpyVcr8UGHuZySuehVVoZTDK0heF9vn1s1U+vPqhaa3Rr9fUB8jVZKvEVL3Uj356DYH8CqP3xBRxSmAuY8yslTnToCtBZpqlgb26y/QzE1ln+rX3v/s2Ue4J6px6L6lu7jIHK3SpfUvZXNYYsI6sTXVAYP6/jo+OZ4huGufImSpifjjbJ4pCqihU22Csom0b+r7f9wFrAJ8GbueHqwpZi03QpKAxhdT98rtr/O4I4kgat6SxJ/Q7svekceTliwu++OJLfvm3v+Lq5paQMqoJdKM4wscCEdEujVUCDLJQvCtNW2thNG+3W774YuTfh2vOlpZl63jw4IRHb7/Lg0dv8/id72EN6BJQuRpHDzsRIo+BNIaK/ihyu2ChCx+8+xbHq4UcMvttVYOHol00IjIcAjSWrKV3sjKOhWtxi5aw8xjt+P6H7/HWWcuJHXl28ynjpmXnTlidvUOzWLNYHQlJTRm0Ugz9jqHbEuJISQF0ZOEUrTY0FFrrcNoRSmKM0jNcpohKkdthYKkbsspcXV1RckKRSSmQsqQOXb+l67d37ljOUh3MfX9dUNUIG5VJxZNLJIdAKr4mIrpa62QowoK21rI4eYRp12SsKDCFgF0tOTo5ZbU+QjcLQYFQxNGjNRytFiQMISteGBiTONcPgxBtxjwIOa0kWluwKmK1EDEyiUwkFy9s2YOn3Rp5ZmpePPegrK4UfjQxF3wSRrRW0Li63yu0V5QUEK7k2gqKFN2Qs8ZHhyoenQtOV6b1pKer5KyM9VpThTdrGl/ZpQqMvJe2tTw8O+a9x6d8+OQhi7YVVAfFrr/b6xSLMdBWMDmiZ7zd7EeiopwlaDXvWaGV7YuAKWHQMM8u3xXygEnRZWKA1n98J/BNX5sDoVI4Y0C3TL6YomK1fwmYtE3/DtOCg/VmQVDB6fkJj8+PYfBsrm7pu45UCsbKkLct0hxSqWoKxkwZrrFWc36qcaxZOIMi0G+FTWSbhna1wLUNbtFKQa40292O3RC46QLDOMqGt5r1ouVk2fD22qFtJoSCdgvQjTSQVcIi+L4qMjzf1jGOOI64lFlMiu5fd5kHNypVGvuEc88gz4xzv348T0oV0lMch1HmFk2aTSOddl8hwUxQ5xTUpt9f16B8/T1+E3v04AfnwGmtETX5WWKieoRpGbVQak1OLcE35CgzcgmxODFmP7tY2FfNxmhRjdF6rjy1kWHhiQ49iT43TbuXNKqBWSpbsf3RRnrEIJlkUXmGjvWhskopjN0t3eaSNHaUMJD8yM31lsuXF1xdXPHLv/kl15c3bPoeZQyNtRhrWLZC7hhGz5Chzwq1lfvWNIbj9QJrG3bJE32EGGVGMCS2vRBJXD9y0+/47MU1pvkEt/g5bWM5WhlOj1oWTcNqdUrbCsml63bYCiP7orjejbz44pIcPK0zc1pcCnjvJbms1ZB1luSMDBZbjRoyxlqWrqHYIIxKDBc3O7bdwDAmNB3GD4R4SzKO0S3E668KZE+qJmdtVVophgViPG2KJYaB0Y9cXd1IhWI06yzei70P9DpRSmS322GMomnsjAqUIr3CGMId6CsiRBMdE2ip6hpdaJy0RUKmWkuJ2lPJwtDNKTKOlhAj1rYY11QlIEWyC9rzt1goOD2bPBNFzkss2CKhus40zlKUpgHe1q28doF+yIRgGAZDNyZCKhiVaK2itYp+2xNDZBw9Knq0Z3+/gCFk0KKva83EjJ+71kyjPq3VYhGl1OymIvQZmQVUyChIKoWQErGqXintUFg0Gh/H+thKIjH1C0rl7MoccZ1zrfrK2Uh/tG0cv/vh27z9UM7vVbuonASx6hrHePh4EWLEhYDKBWXr86mUQIEoSDJTbNDYLGMi6qDnd0hIKRX6pJ4FMCFr+z7/ocYuiP3SBAWpemZNEo8zX0BrFDJPKAhirCa9dTymFAZ/N7h/23qzYfkCL17dkELiZtNzfbOl2/b0fY/VCmd0bY5O0/6C15o+CG1aGxqjsT6w6RW3Nx0pJYwLNF3ENo5m4etHqHhxuaXrAzfdKIPBOVO05mgRuF21pNGhrSEYg240aEfWBV0x/djdyrxbjrTWooAwBHbbkc1wMMdUCj/72c+kX3fnesvM5vwmcso3f1ZlNkGdBuCbpqFpmjsw5iHM+VWIkzs/M/fM6jqEb6f14sWL/cxhzvzNL35Bt5NeVU6xkl8qjl7KPCRfsrhBl5yIwVe6dUZcqAVaaBo3B7uc8v59a3UnoxNfsel65MQopQj8VjNENV1vhR9RWpAAFOKoXm26XIvShpcvX85s3lwKf/nXH3P56hXR96gURLruesPFywuuLq/54vNndLue3bZHTweDSeKbpzXDGPAJ+lzo695qgGYM2CSGvaOP+BDpiBiVeL4ptFb8ALVOFLZkDEVfsWgtRyvH6fGCtnEsmpV4rGmpvJwRiHNMmW5MPNt4brZbfDqcEyz0XTffA+9HUsmkqCnRoowhj/LzRgtRSaXCbz77NS9fOrRRDDeXGBJWyVA0SqTBJiWcMs2sKVM9NkV5I8cBckKVhPcClV5d3oACYy1DdpjFkmAUGwdNifz68y8wRtM0lpvNQM4KbRwvXj3n6vKC6cJKgedXt3SDl75g3ReNMzRtDYIFxOtQeveiEZ4Bi8LgmiXGOIxtcE0jwTaOlFIwWrMLVce2MsZLFr/LWI35nGsnlgoxi6RbAYZxrEpOic4Xgrh60zpNYxV9NxJDwo+BnDzXt7s9GlQKt7uB0ceDIEg1nGW+9v2hXX9X+8pYYNAaz8pEykrECoGHFAlBzHaHONZAIC0h6b+reRAgK5kVTFMQrMHYaIVOmbEbuXUdpsC16uTeU4gh8fLi5qAQK7y63dGOfk5OlTFoa2sRoCDXwK+1+Lpytys0B8GDnqCaymVmcP6gcqzOG1k5jroAAAFdSURBVLUNcZhUH5TZUhGqQ0chNTvAxOpIIUIJkkS82u6+cjZ/01LfRMsHUBNfnteDQDl4gL/jOsSADr92uMqd3775R78tLh0ileXrvwV8bfCZf+5bPpvvur5Lo/bvs6aqcVr/EAWF/9bW3euqDyNw507OW/LN9+ab3t2/8869tikP391X32nhH7StJvjo4MD9+ofqW1/k4M9v8u/2KIicR9/8b17/3sH59y3v4R+2vu6Vyrd8/e+1voL8/Pez1DffBF7fj298V/7//8F/2PqaPXm3vVi+8Y28cRC8X/frft2v+3W//ntc3xYE/8cpF+7X/bpf9+t+3a/vuL61Erxf9+t+3a/7db/+R173leD9ul/3637dr9/adR8E79f9ul/363791q77IHi/7tf9ul/367d23QfB+3W/7tf9ul+/tes+CN6v+3W/7tf9+q1d90Hwft2v+3W/7tdv7fr/AOrGIFN7UE4jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:  \\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\n    images[:64], \\n    padding=2,\\n    normalize=True),\\n    (1, 2, 0)))\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:\\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(\\n    np.transpose(\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\n    )\\n)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/plot_visual-check_basic.py\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "\n",
    "\n",
    "for images, labels in train_loader:  \n",
    "    break\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training images\")\n",
    "plt.imshow(np.transpose(torchvision.utils.make_grid(\n",
    "    images[:64], \n",
    "    padding=2,\n",
    "    normalize=True),\n",
    "    (1, 2, 0)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872ac6a1",
   "metadata": {},
   "source": [
    "### Setting up a `DataModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e108c5a",
   "metadata": {},
   "source": [
    "- There are three main ways we can prepare the dataset for Lightning. We can\n",
    "  1. make the dataset part of the model;\n",
    "  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\n",
    "  3. create a LightningDataModule.\n",
    "- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5dbdca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_formatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
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       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from torch.utils.data.dataset import random_split\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "class DataModule(pl.LightningDataModule):\n",
    "    def __init__(self, data_path=\"./\"):\n",
    "        super().__init__()\n",
    "        self.data_path = data_path\n",
    "\n",
    "    def prepare_data(self):\n",
    "        datasets.CIFAR10(root=self.data_path, download=True)\n",
    "\n",
    "        self.train_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.RandomCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        self.test_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.CenterCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "        return\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        train = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=True,\n",
    "            transform=self.train_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.test = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=False,\n",
    "            transform=self.test_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.train, self.valid = random_split(train, lengths=[45000, 5000])\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        train_loader = DataLoader(\n",
    "            dataset=self.train,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=True,\n",
    "            shuffle=True,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return train_loader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        valid_loader = DataLoader(\n",
    "            dataset=self.valid,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return valid_loader\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        test_loader = DataLoader(\n",
    "            dataset=self.test,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return test_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6d2769",
   "metadata": {},
   "source": [
    "- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \n",
    "- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d9751726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1) \\ndata_module = DataModule(data_path='./data')\";\n",
       "                var nbb_formatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1)\\ndata_module = DataModule(data_path=\\\"./data\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "torch.manual_seed(1) \n",
    "data_module = DataModule(data_path='./data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6592489d",
   "metadata": {},
   "source": [
    "## Training the model using the PyTorch Lightning Trainer class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cbbca2",
   "metadata": {},
   "source": [
    "- Next, we initialize our model.\n",
    "- Also, we define a call back to obtain the model with the best validation set performance after training.\n",
    "- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "544db16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_formatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "pytorch_model = PyTorchModel(num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36da6429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(\\n        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n    )  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\")  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
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       "            }, 500);\n",
       "            "
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     "metadata": {},
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   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_acc_basic.py\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "\n",
    "\n",
    "lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        save_top_k=1, mode=\"max\", monitor=\"valid_acc\"\n",
    "    )  # save top 1 model\n",
    "]\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab2f480",
   "metadata": {},
   "source": [
    "- Now it's time to train our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a7804d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n",
      "  rank_zero_deprecation(\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type         | Params\n",
      "-------------------------------------------\n",
      "0 | model     | PyTorchModel | 7.0 M \n",
      "1 | train_acc | Accuracy     | 0     \n",
      "2 | valid_acc | Accuracy     | 0     \n",
      "3 | test_acc  | Accuracy     | 0     \n",
      "-------------------------------------------\n",
      "7.0 M     Trainable params\n",
      "0         Non-trainable params\n",
      "7.0 M     Total params\n",
      "27.856    Total estimated model params size (MB)\n"
     ]
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       "Validation sanity check: 0it [00:00, ?it/s]"
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      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training took 21.67 min in total.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/trainer_nb_basic.py\n",
    "import time\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=NUM_EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    progress_bar_refresh_rate=50,  # recommended for notebooks\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "    logger=logger,\n",
    "    deterministic=False,\n",
    "    log_every_n_steps=10,\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "trainer.fit(model=lightning_model, datamodule=data_module)\n",
    "\n",
    "runtime = (time.time() - start_time) / 60\n",
    "print(f\"Training took {runtime:.2f} min in total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420f571a",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c761bf",
   "metadata": {},
   "source": [
    "- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a17f048a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_plot_basic.py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"valid_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "df_metrics[[\"train_acc\", \"valid_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc12c76",
   "metadata": {},
   "source": [
    "- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f14b73d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "Loaded model weights from checkpoint at logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afba40e70fa049698b41045251bd2ff6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.8289999961853027}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8289999961853027}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\";\n",
       "                var nbb_formatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\"best\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d43277",
   "metadata": {},
   "source": [
    "## Predicting labels of new data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e55397c",
   "metadata": {},
   "source": [
    "- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\n",
    "- Alternatively, we can also manually load the best model from a checkpoint as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56af2723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_formatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = trainer.checkpoint_callback.best_model_path\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c219951c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval();\";\n",
       "                var nbb_formatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\n",
    "lightning_model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56664cff",
   "metadata": {},
   "source": [
    "- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\n",
    "- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "839be5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7, 5, 8, 0, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/datamodule_testloader.py\n",
    "test_dataloader = data_module.test_dataloader()\n",
    "acc = torchmetrics.Accuracy()\n",
    "\n",
    "for batch in test_dataloader:\n",
    "    features, true_labels = batch\n",
    "\n",
    "    with torch.no_grad():\n",
    "        logits = lightning_model(features)\n",
    "\n",
    "    predicted_labels = torch.argmax(logits, dim=1)\n",
    "    acc(predicted_labels, true_labels)\n",
    "\n",
    "predicted_labels[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40953be4",
   "metadata": {},
   "source": [
    "- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f5a368c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.8290 (82.90%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"test_acc = acc.compute()\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\";\n",
       "                var nbb_formatted_code = \"test_acc = acc.compute()\\nprint(f\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc = acc.compute()\n",
    "print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d97bc8",
   "metadata": {},
   "source": [
    "## Inspecting Failure Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c239efb2",
   "metadata": {},
   "source": [
    "- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\n",
    "- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eac623cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"class_dict = {0: 'airplane',\\n              1: 'automobile',\\n              2: 'bird',\\n              3: 'cat',\\n              4: 'deer',\\n              5: 'dog',\\n              6: 'frog',\\n              7: 'horse',\\n              8: 'ship',\\n              9: 'truck'}\";\n",
       "                var nbb_formatted_code = \"class_dict = {\\n    0: \\\"airplane\\\",\\n    1: \\\"automobile\\\",\\n    2: \\\"bird\\\",\\n    3: \\\"cat\\\",\\n    4: \\\"deer\\\",\\n    5: \\\"dog\\\",\\n    6: \\\"frog\\\",\\n    7: \\\"horse\\\",\\n    8: \\\"ship\\\",\\n    9: \\\"truck\\\",\\n}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict = {0: 'airplane',\n",
    "              1: 'automobile',\n",
    "              2: 'bird',\n",
    "              3: 'cat',\n",
    "              4: 'deer',\n",
    "              5: 'dog',\n",
    "              6: 'frog',\n",
    "              7: 'horse',\n",
    "              8: 'ship',\n",
    "              9: 'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9dc59be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PU7311ltfUwor8gqHXCtChVpBoCJtckTaCgm1CtLGSfShenr01qfeesuvPPx3H8+TJ0/udE3/4z/zhbrf3TDu90zjgRACjx4/5vzsjGE1EJzFCKd1cNZgjcFb/REBSianhVoyITi64LBWsCKIgIjBGCHnzDiOlFLwzmGtxViLsw7vLIJ+TkwLMS4AOCdYa7HGUrGUCsuykEvGGn0P7z0ViMtCjpHa3r/ve9br7k7X8/d++1+o1nqCDQwhYKwll8o0jxxuXrBMBygLhoyVdl2VijEOa4J+J+cwVjCm6l5QE8YYaoUUI6VkQDDGYK0FoJRCrZXT5VT12hKjzxPR19daKOXW42IwxuC9p+87utDhQ8A6i0gFqVSpYC1iLT/tJ33+TtfzF/6KX1wP1zv219e8uLoixgXvLavtwOpig3jPmAq5AAh6sVrm3cR0tePmrWeMNzeEwbN5tOXR0y0XjzbYaqnFMI2FJUZyXgi95exyzbDusM4gCCVVas6UUnDeY72nVkucC/vrHfubHRhD3/f8uG/5Oj732UtyWvjyl97ir/yl7+TNLz8jDAN9FwjeY8RABdv1+PWGP/kf/fGPdT1frQdVk25upQIGqqGWBFTEoDdv28jEAChdhRUQaxAMtVp9HaLOo1SKqHNRB1WRKvr+Ri84cJRqSdUxJWFKsORKzIUYISegHB0TzTlVnAjOGpwRrFWHV6UA5bQxizEYwH4cq3kP9tWUaEV0rY/nQ97L2VShHOlN2o0u6LkWqe+agHz/z9RN5f7mpo0UrBSsqfojBScFa/S/zhiMALVSSkSKwYjFisMaoBZqzUgtQIYi1CxUEaoYKhVjCmIcIhUjxwCoAG3NaqFW0QAoJeZ5Ii4z3hosgeOaG6M3jWnvYKRiperxAUV0pXO7ts09LGtBP7tIpUjGGLAWghhKHXBBkOiwacGXRM2ZWgrVCjiLdAHxXr+7re0+LYjoVZhSouSi970xOG81QKqVd1/a6oAEYwREKKXqZ7WYC/RxawzOOUIIOOew1mCkIlKolFPAeh/z/dYafPCELuC9w5jCsO4YNj1d35GNwZREOX5fY0EMUtWxUPU6sM7gvEFMuz6qo2Yhx0RaIjHPGOMoedD1zYWcCssYKUXXv1tBZx3GgDHN2aeMcbqG1lqcte0eEUoupFhwrlCDwbmOrgsY5xDvwYWPfb1eESShF5JG4AYpRi84A7S4u+37AKcLTEQwIgiWikNsQKoHMlKhEsnNZwim3bAGqYYshoInVcsYhUOEw1KZsyEWwxIhxUotgtSCN7x0TgaCE4KDzgvWCFJN23yLHrPoyflRUetstHBHdrh668+1QkqZJWXdKFoGQKlYA8ZoZnWPvuerMkNpG706qpPDkoKVjBWDNZr50xyRoWIRrLSbtCaEBLVASVoNyJZqjpm4oeWf7VL/ipWkolH9vMxM48g0HsgxMvQdwbXAy4g6ndN9U5F2wxhpQd6t9/uhgcXdmLrd9p0oVBGM080rmA7vwU4VN0X8YaHMCym2QNY5vd+xOBF8y5JOSVGtFBGKMdRSkVadMca0faG+9COtMiPoXgKQTaEUgVL0edYh1iJWszZv23sJ0NZXRN/3uLJ3bWKb8+wCoXOUgjqndY/rnO5jRq9JY6xmii1YrClDyRgRnNHMv5ZKilmv1QwpJtKykHMkOyBXpLSrNBXiFCm1ItZicyUc11c0OC25Yp3e79YK1hooFmuMHo+xGOsJvmMYVvSrAdt5CkIsH/96vpKDMlRMI/BzUpqHh9ruVDEawRxLRKUWyjGjEgPGYlygC2cEN5BSJMWRec6kpGm/wVL1kyjVkIolVsd+rryzm5mSJUmn5T5xRGtJCClGclyoccGUhDdgTcXUiLeFzWBYBXVY3rnT5nW8cO2nfOP9OKwK6phqbdtr1Xu7VnKB3WFkt99jnaPvO0wrg3Uh0HmPs+YU+X5aTWrCkBGyOh6pWDK2JkxJmCpYDEhBTMGKOiYjWsKrkqmSkZq0zJxFS0Lt+sglU1uELtwKvo6BW62UoqXjq+sbnr3zNtPhgFB5dHHBqu9xDo7RvtrL1x5Lj3oXtcysFmotX1W2/HFbxVCNUAxkdQ+YKoDFS8XYTHAGGyd49gbpZk+eI/5sS7i4wEw9WA/GgxicCJZCypmUM1IrjoqpBnEW0wXECpI1cxOgOku2rWRa0M+vkEwmSUZSxKaCbM6RYUsOXp2j8HL/ASxWM5FqELEgdw9iTikiVvDB0/WBKpZhM2D7jiqWUipGDGK0FIwYSq16GeSEULFGr7maK3FMUGYcIMUQp1n3wZqR6nFi6VzAGUcyiZoNqRSKCM4HxBmOCytSTyVU25y7FYNYSwgd6/Wa88vCer1le7Zle7YhDB3FG+Z5Yd4fPuzrv7K90hkSY9F71bQbs91XcoxGbkXY7WaSk4dWBxZC4Ozyks36kvEwsrt5zlIWPXHVIliojpQNMVemCFMq7ObKiwNk47H9mmo6MAERp1mRXagykpaZWmdizUjKSBF8zhSppFpZIfQiBKNprGZS5RS93rV9lHLYR9qomnNqddhW0oNUKjlpuXS/H7m6vsGHQKkFby1WBG8L1bUTeSoXfjrNSj05JiMZU21zWKk5L8HiWlaSdTmKwZxyd3VuQkFK1k0uQ231tZwi1Rhqce3cqVNR9uUC1ZBrIZbKzeHAs6trlnHEWcNqtSZlDQpK1axdeOmU9PXNISHUUl7eRz+kD3g3pp+o5c6CI5dKbWVxEaM9JrHYUqnjiDkcYC4Y73DBYcYDNUNBsymsIFKpObXek5b0bdX+Hb3XQLdUpGhfu3gL3kIRpFVeqYAv4ArMEzUVbDGIGSidUMVSqz6xSHP4VTCna/hlIH2XZgAfPN5apnigkPB9QLxjSVBKRbAtYHLtXtVrouR8uvdLLqQ5IkBJlSRgxFBKUcdiHM75U4kQa7X82WVqbnqFYvStpV13rVVjWpanPVGHM8JqteLs8pxYLcN6zWq9YtgMuM6TrZDIMH786/VqDkp6pN2o5VZJr0ppGIWMiEY6pdQW9bXFqNrvC8Fz+eQxj1/7PFfvvCDVwn4ZSdNCrYZSHbU6pqVyc5i52c/cHCJTErLt6dYDIVwgfqBajzUeVw2xWzDdjF8WcpxY5gMpzlr3lsS+zKQ5kWohFVh5Q+fAGmmbdfxYF/artTvt2citqL0KNRdSqcwps8yJ3TizO4x0peC8w/QGb61GVWJaVH8sav2QBoH+526+yfuaPWbOVO0jVY06TS1ITUjR6FRaKa/mSilC9dKi6qKvK1nLe6L9J6qlFkjLAkbwXgENLQWl1kyhUowh18oUE9OyMM6RFDMihlIgl0ouFZMrxrZeTHNOpSg4oJQGECqJWgqmZWn3ERhIrUjrPdfiKMYiLQKXatRpSUDQsn21A9JbinGkOcE0UQ4TpRiqOIoVsq1EyUTR0pwrYIvTzdQZigjkcirJZWfJ3kIVTKpI1uss9UIKlTpNyJyptce6DXQrcJ7a+oNazK9U0XC0UhvY5e6FZXtnGYYBRJjTnrnM2GApaI+nZjDtf7Zq+FLQXn0uhVIqpVbSksDMlFzIvmBswTmP99p7894SVoHiDFMt2Jo1zHBW48zSQGJLBCPNaRXEVqy3uODx1uFNwDpYbwuXTy8pzmOcw3ce0xkIDbiyCN5//Hf/KzmoufaYCrZFJU7yqV6uOSgNANGivVO/ivbvTCmJlCLLEnVjzDBly5gsc6zMMRNjZZoLuzGyPyR2Y6KIw68Cwa4wYY0LPbhANo6CYFPAOk+yM8lZrdtar2WqMjMumSlGllSJuVKrRsCDsYgk5CPzft6vac/vK1F1xx7T8aHjv3PVizuXQi6VJRWWJTMviWnWzXRJBUmJJWdC0Zv6h8TvwntHn58CJ2WNZlEihVoytQi0jEh7Trn1MEr7W1HH3TKnI4pUw3QFO9SSmwOppLS0MozgnCW1qLbWinMO550CI6aJm92eF1fXUAvWWl1/1EmZUnDN4dTm4GrN5FSxFqhQcqLUouAiPmLm/DXaMcCUY/+5FLKRU89WgbJCFQe+R4aAcQPiC1kSdYlUEbIVPTlOy4WpFo6xazGG3Dr11Tt9XqZlkpXqLdV7KJUiBTHaAy9BqEF0m6Hoc6w2lNUBHTMlWg/8uBVJC9buejVbr7sBlqwzuKyBjwJqjr10RcbVUlr7pAUw+WXAX6sCRETAOAWXuM4R+o7Qe7y32KDrkUolZUVKVqDUSomJNBWWWuh6h/MW6yw+tN+tadmTJ3SWValszjaMRXNpYw3VaXLmnKXrAgyr9/3eH9VeyUHtlp5aMkYyva90FoLRBrOpLUqp+dhzO0WGUgVqIZfINO14580vsdup83mx23N9iFxPcLOPXO8Tu0NiWioZSyqQq8O6nhB6bOhxocOFgFiPMxp9OKO17SlnSrb0w4radZQcmWcYxwNxroxkllQxWLy19MFhxGDr3UdTcLxh3mvjkQ/d6Y89kHf/9SsawKIN/VIrS0xMMTLHxBIVEBFTIS6ZeYlMMVHEkqshxsziM8FVUtVN1doPLzLdewYluZX5jg4KzexrbsFTUucqBSNaAtIelP47k1G06ku0V6lQqnZgYozEGEkpKlT61mkKfSD0gZQj4zhy9eIFb7/9NsF7+tCRSiFTSRSkJFyxCthogVstCsowJiMilBwpWSHbx77WXZvew60bVKGW1kyXghWwZMzR6fcDpne44QxsotaZ6iy112Cydj3ZqjNhmjBLRKHU2mPKoYOhQ7xDimaWhYo4i+0CNWVKzApaMUJDRGFiQmLBnJ1j1muks2ChigEMrlX1jkjdAlSxmjHfscWcyIe9Bhu5YCrkOZIKlOSoVXt8NRdSilRt+Dd0ZG79STBWcMHSrwP9tsd1HaHv8F3AeY+xAi2wyaXomlVBKqQ5sxwm4jRRYmRzsWZ7scF3jiodzjrNVsXinKMLHbFWwtDjpwWpueUiglhD8IEgjhX+Y1+vV3JQ13Ngd9iR08h2BWeDcDHAYLW2K0Lr5bRIWo4/rW5aCynOXF89p94kxkXYz4XDUlmKZ0yV/VK4mmBOggRF5VQDJvRU6zHe41qD0VhPqZWYErVGTI1YyeqsnIfqmOdKXSxZPEsJLCmTc8HbTOcs685gTL2XixVazfldu7puoPVlOe3YPzo9fvzluM4v3yCXojfgERHWXqgZU+YwLezGmWmJLDGTSyWmQkpZN96Y9T1qJeVCSklhrCutTWvjtgUj3D6uW1/iHqF+0pyRlEKJkVIL5KTjEKfESDDNiYk0B4UCI7S093IzAGmlIr0pU1oYx5FaFSZunM5EAeSacT4wz5FlnJiniWmadF5qntmNI1e7G5bF450hlzXBe1JOOh+UIlXA2oJB9N+tV1isoeS751DVMr1pyLcGWqiaTRVTkRSRcUT2O+p+h3Q92LXCzIuBEAADPkDXU63OQtkla9nVKHgK58EHajeAdzScNRjTeiKWYhJIohioDbCjyFKPCfrcmicYC5SOGjqwrlUBIMvx+FsuVe/+Oh1TYjmMlBSx3iBW7/WK9nuldUNLycR5UQdVLSUlKEVHYpyjGwLD2cDm0ZrV+YDrAi4ErHeIGO0rL5k0JUXmicG7QOh6conEcWHcjaRloes7DdKDRYxpSGcwzuJDIPiOrlZC6AidR4ql6lnFWU/f9VQi8yfA8ftKDurF5PnBt0b2++c8vnB85jIQXEewBm/0hj+mr2KkoWU0Wi3t4s61MI8jS66M0TJlSzIO/AoTgqJ48oLNgu0CYiw5Z8RYihGwQugc/eDxzrMsCyVGlnggLwumFILVgchaYZk0ehAbqDbp83OmO0QGK5wNHudRZNc9WM5JYe+3egwips2VyQmX8JUmp//K6f81ayq1srR5CTEGZzSDyqUQU2oOauIwRWJqdencnFGMpGWh1oIdR/bOsusD8UKbsdZZXHA6kKkf+EPsFJ7Iez/+SZvkBSkJcqTEhVwsNUVIC5iCZDBZAT62ZP0vgq0JyYqUqjkpiqGVh6TNztVaSHlhWSZiTCByGkKtIsw5E6tuDNN+JC76PjEmrvd7/DvPyDkSvKUPlovzCzbrtSJISyaniDO1lSnVQZUC1Sh0vt7DJXqsgtSqaK9Km0+qDcSwTNgXL7BvvUl+84uU9UB1leq8XripULPOnlUiRYoGC4cJs18UGOEs+ApZy1vVOr0XrMF3oZ2fGdGZEkyw1OAgVWQpiv5zlno4EMkY75HVhnpxSR1WILbNVDYUcjWYUtvYwN3afp65ef6CZTywOVvTD52iip3BOgdYaoKlleFyq0qlJVGzAiDCENhcbjh7bcPq0ZrhvMdaoxBwY0ixEJfItD+wf3GgxEIfBs62hmEVKBLJU2E5JFJK1ALOB6wXjK2UYhBXscFgu4D1AZcL3nu6EHCtm2MqeN+xCisO0479zcePknglB3XIgatJuNoVcJV+qDxNhlQN1ehRH0tL0mC0IgoXLxVShaUK05KZ4kKsHVkCxq3wpsOnSMgLHQuSUQclQk65pfuahUHBW2EVHKYmkhSmEilRZUuMMQ2dW6kltcYzFJ13p2bhMGd2PrGfM06E4RNo8H01lqs2wNuIJ1BPTudUM+flL/KuqO/2c7SMF5nm+SUSx+qulhusV9N9tAZvbJvoKZgqbQDVUnMlp8zcmtSHaWboZ1ZloAitYd/Otdw6itabul32umszJSNZfzjCcnPGl4LPBZczJiYd0M9Z5z1EsDkjpiIpQVJnZY7D4kUUiVa1j2VMxVgdFF1iJM2ZOWZyuaYizNPCuJ95851nPH9xQ6ZytT/w/PkLVoPX+TwrPH70mMePLrk827Ieeh0UdoZk1RnkuJBzodQZkz2F+8igWo+nKAODtGakyRVXM3ackOsbzPU1ctiBZMruiuo7ivFQdF5SovZL8FVh/DlRq96bkhWYIqVgYgajSEixBuk7pFZMztR5hhSRziG91w0lgvQ90nek8YYUJ4xzmCVhQg+u45gtVSmn71KMAlXu2qZlYT8eiNNIfzY0RguHM/YUiVaBjK51zZmUI2mO5JTpgme1GTh/suX89S3dtsOv3KmKUmIhHxYO1zfcvH3D7sVBeQk2hTwMWCM44zBWEXrUgrNenaRXpHZF8MGAryQpVCPKkOIdwfvW19XhYecCznrSUrh+sf/Y1+uVHFSUjmxWZLNmqZYle2IJpIoiomptTV0FN+mQnGtlCmEulX0SxhnmIhgXcN2asD4DOxDrzFInZmZsrkiLwrJJ5JQoKZJiJM4TrAeCFbCWxQqmVkVdAVTDkiI56SR/beWTnDJgqGKZYmI3FW7GRHCWcE8OqojRCE9Ea/NyRBi1Csfx9wpwbOy+PNbbR11rZZkn9vsdfd9r1Fr0FJesPQ5nhC4oEic3Z5dzYYmJ5CzJe619ZwUWWKdg6Fhqa/KLIjiPO9WtozgCMu7TSZljZF8ythZsNfgKvgihCi4VpMS2pkVZB9AIX2qGGCFmnbURw9H51qiUXk6g7wJ9b5lj5no/crOfeXG95+p6x4vrG/b7A+Nh4jDOTPNCLJl6q9dV84yphdceP+Kzn/kM3/JN38jnXn+N7WaFDAFrrAIJlkhadK5FukDH3SNNhSPKsNJQ9JpNl4pfIvYww+EAy4IzFsmVuj+QfaG6otDyYtr1lLGDhc5QbCX3oAFZwuaCmxLCgsY9ykZRRqvZTsqUZabEBekcpg9aeqwKiijeEMeRuN8hzuGqJWwnTB81qDM6+1ZF6dNAkal3bUuMChCxgh06wmZFZzzS+k61KFhLSQasDtzOC3lRBgjXOTaP1py9tuXstQ3ihWpq61kW0hgZb3a8eOMdrt68YR5nvPes+kCpUYfQnRBWKwVhRMeq7/DWIbY5o87Sry3ZZcYy0dceBJxVB6UEDQaq9keVYily9fz6Y1+vV4OZ+w4bVoQhcna55ewi4PuKMROlRm0wS7uCjaOKp9IhPmCrJ06Rw5JYioANhNU5/eoS368pePwsWFewJiGtzHLKx1rDc5lmXjx7TjCGvg3vOWsJ3mGNYY5JT2qp5KyNZRGdiPbOUIulZMOcKvspcT1G+g7Ww8dP0/HVWLEejNV5D8XYnJCRL13AbdYCvsJJnZBdor/nnEhxoXjfXq3f31lDcJbkHGDoEBCdPUk5E2Mm5QZjLYpu0/EJoesCQ99hQ0cV0+ZK4Csc5ck53crq7sFDjfsDyzgS54WSkmaV08joDGU50hmps3DOElxDLXkQU4kpk1KmYvW8tGym1EoqOlBeK2CEXCqHceHq5sCzqz0vrvZcXR+Ypom4JJYEGUfBaJmMiimVZcrkeWyZWaXznpoTjy7POd+sWPUdViDOI3GeSMvIaughLzy94/U0R9YVxWo3wJOe4QJE76jbDVleI8Qt2QrRObJxZOdAHBXRjLYWTO+QzpL6Xkv3aKnIZeWBtKVRIJnWhDdKwVVyJs0dKS5IsNjOt2vRYjZrzNAjJeOO99NqQ/GeYvS8annveEcc7e4zqBMHXu/ZXJxzdnlJbwM1VZZxZB5nUoxAoescVA8lsgRL6T3rs4HN5ZrVWU/oFUqfWlJQU+VwNXLzzo798z1pjHTBsT5bc/54w7AJLHkm54z30G8HettzdrGmHzx0Be8qtjP4wSM+s+SJOU7UqFVXI7bRwx0DFyGnwjxF9rt7HtS1vsOHntVGeO0zn+PpZcfK77D1GTWO5Br14I0B6yl0VHpcOMOYgTLvGeOeIpbebxnWj9meP6JKIOaKcwkjM+RKWTLFFIWLi5bspArTOBH3O5xUeq8zBbY185zzjNPM4TA25BV6PCIE79pEdmZahCVWdiXS7WA1GC7vqwelO2Pr1yo8ukibAWn9PFOP7akj/8NLa8Hg6fdS2kBfURofqQUjluBtQwlXTIPpWudwodP+VMyU1qBF0El2Z7BOz6cx4J2liGnITE5Aw+Ps71c4UrmPMUi4ubpmnmamcdKZJWvZ7/ZQMsFaqOpkvDWshp6u84TgtWwn5QQQwTjEOo0QRTnjYk5EgSwGijAuCzf7kefXB55fj9wcFqYoZAnYEAhOex6+nTtnwdTMQSxjzixL4urqmi996Q1yikyHA4ezDeu+x5mqva55JE4HtpsVlMw33/F6miMn4zFaqjQaIohGSEPP8tpT3ONHJNSRLFHpdIptYAYjyulYKsY7cJZYMqkoWtEh5KJAf4tQjJCsaDQPxFpxpZAWHei3RrDeUa0hO4MNHdZ7fDfg56yowC6QVgPVG7D6Pk7he+Rb98tdW62Vru8Zho6LywsuHz9iCAN5jly9MCwxEfOeWguhV5i3q4EyeER6tpcrto/WdOuAcUbv50YvWmbYvzNy/eaO8WpCEM7ONjx6/RGPP/cY5z37nfakvIGLs47LTU93tkZWnrqq0FlMAOcd4jIxTYzTgZotKVeO3fojo0wpmulP08x+f889qLPzR0xLJi4Ll4+fcnER2IrDL5Fad5S0AJVcLVIDq/VjNmevs9o8xffnrK4ObJ9dsTtM5Aqr4Zw+nDGnrI3s0gbUmpOraL0410xKkbgsmJrwFFJMp81ExOJdoB8GYsqnn5oLiJZvgnMa4bdZjpIWcspc72eGYNgM96Pd+Ff+6ndgjdVeiDOEYBm6wHrVsx16emdPXGI6WnasU78cdDreaEWqIny6Ti+wRkYqorVl7w19BecqVaySQTpHqRCPwYCxbaCxDfK1DSflBr1uvZ16dJ7W4NscRN91dN6dZk7uw37wS29qGThGxvGAiDBOI31o/bhaqSXjnWXV93S9JzjNrF7O9IFpvG7GaKSeUiSWoqwG1lONYz9FrnYjh0nnxwoG44KWm6VqUzvlUxasUUbBWkffrbjYDpytB/o+UFs/axwn8jJTUmR/2DGNIzEurG9GdlPmp93xelqrpTFOI646G6azyxbTdYgN5FKYSyHOM1OuSnRr6kugnIjS6RWhxja6UJUf04oOpToRnFiqMQqIakwdbVBI+8dGZ9BsNdQi5GKwFQIWMwR838rl1pKdo4pFsazHJu7L73YfDsqIIve8CwTX03U9fT9QfKCiZb55PzLNIyVHbCl0znK23TBsBs4fXXC+2dKFDoulUIg1Mu1nxmcj++d75sOC85bVeuDy9Udcvv6IzcVGg4qUwFWcMaxWPf1mwK866B3Fa28uzZE0ZVbrHtPpPFvJx2oMHPntSoGUM+MUOez3jPdNdXR2fkkqkHLi4vIJ2zPLulY4HFjmQE5W4afVUcSxXV1y+dlv4eLR17PaPuFyN3H59jt8+a23ubq6IQwrgluxLCM5LVAEK0oY6awSIZ42yXlhmka8VGywbeNtxK/G4EJg1aDRMWfqNJ8yutpKXDR6I4AUF6a8sDuMBAur/uPH8H819pf+6l/FisM5gw+W9Xrg4nzLa48f4Z8+JtgB0+ZBzCldagQ58pX5VEUjn24Y8N5jrWsZka6TbxxwvnByRsaq5IMxbdJedKA058QSI/MyMc8z8zyT4kKcmwREzlSpOGcZhp6z7ZpHlxd4a1H6+hMo8U7ti298mVr0molxoVB4dqPUMeZI0CrgvWXoOjqvQ40Owd3iIjPWqpMyOo0YcyFXMF1QyLQ4xiVx3RxUSg0q7OXkiDIJ8hHZKoDO8FhjCMPA48ePuTzfUFLEGM1+Y1zIS2EeDzx78Zzd/kDMla6feHZY7nw9NYBRxQJu9R0rgLFUYzHeKC/csjDFxAEty0mVNhzecvMq5MYuc3Tb1lqcVGxV5KK1zR+K9jG1tVzIuWjAWRWBaWptbZCMM47sKl3X6bDw8X+WE3DniIg9zhF95YjE3Zk1FikVKxbvPN4HpYQKAes9KWaunt1QdiPLPOMFBmvYbAZs59henLNZr3HuCFYomCyM+5H5+cR8PVNipl93bJ+ccfn6I86fXtANXsukSTk2u+AZ1h1u6NtAr0VI5FxZ9pE4R87NGhN0jUvV4N5otEstrcRbItM0Mo4HpvGeM6hUYXt2gfeO7fk5XZ9xaaTMGmXWNqEdkzJBnOUOv35COPsM3eYJyIE4C7ubhWlMeNfjXcDZ2EoJYI1hGBQtlsZJ+boaAeeyLBhvcX5gtdlwdn5B13U61+QL1jmmeVFwRttknPdgjE78oyUuay3Od1jbE2VhXOD5zd3f/ADPn73AGp3cdt4wjQM1ZVY+EM/PyF1HIzuh1Ko1YGlUA7yckVLfJTrA3LIj66zOhsgRZScYC2Ibj3ZDYgiAqcQYmeLIuCyM88w4jkzTyLwsLMuiJZYlkmMiJwUNeGdZb1bknFitVgx9R7XqQO+DVvbNZ9eNlaGQ0kIVtOZ/ZLYGqBlnhTAuKsdiDd5YleIAZSJosF2V1XCUKlRjsV1FnIpQzEvkZj8yTgspaTZfWwm01EyMidjAOlAJRuis0PUdnQt0wSuVT47UXIhR+zOrdY+zwouba1JRQNE8Zfbp40dJfZiF4IjJUmtVB98cxxHMo51hzZZUkkN/FOGp14AO7TeNo5YYuQb4sUcU5RH12zKmko8d2UaUa9oUVm3v28p/VZRjT7Jy0hdjTw7IcEQd3pKLeRfTyl2bvVUqVTBIkwix2ttZbVZsL85YpomraSEuMwuVzcWKzbrDrgLiFHhiC9hicTEwRcdQPdtuRTCe/qzn7PEZq+1aWdJF7/thE7De4DuH814rAk7nnuKY2N+M3FzvWabIa6tL3JkqBORj9oxoX1EMpWoVK+eFnBZyuec5qGmOnJ2fc77dMKx7vJ2xEpTmvvUvhErOlXGMHMbMYRFWyRJKoJAxxuNs0Nqq9SdkSOeDapAYQ98Fci0cZnUaqpsDMUZ67xhWK87OL3n86DHGWsZxJOcCxmKdewnUFoN1HtMa3UpXoxQe3ndEn7A+kmpiN97PFaukjOqgDGgtORWF1ueqyJyaWj+psXNbbSDXI9xbaKo92jux/kjT37Kt25O+xpxuinqk0Gk/c1y43u/Z7Q/sx5FxHJnn+cSeUFOhpFZejYklznivznK9GogxKQcj93b/83x3aJ+eSSVinGGwToUJ0aw8pwJLwUg8yRYEFwjOt0BAeyNYg7EeYzxiNNCxpYJRuP6yLEzTQmwDwUeHXFAHlXIiZUWPSq2YvmPVD6w7x6qzdJ3T+aaSSDUTl4laOzabFWXoeH71nKvDTJmVLzEvdx9EheBALLko4lGMnMrLt4rLWoYzuglaZ5oDa3S4Va9jGtrsKDhoRHDWnsYsbiMGc33JRI5r0hK1tAy0iZEqxAJjlbUbGmGZcOrhKt3eLSDR6ajvR75EjhpWuZxYOYBTdadf9ZxdnjONE7vrHfM8cogzTjrWK0vpDckUSGCi4IvBJstcA9Gu4BKyqfSXa1bnK8Kma/NVSUuLfWhce06Rg2iv3pZCnBb21yOH65mUcgvmBYyOplQKpap8ShFlZdH5i+OA+8fPdPJqMPOUdQbCKlOucxVHh/EB77xqsUjFm4zkwtWz53zX3/wuDlPgM5/rsTmyTDOl5Ma4qyix1dCBqRymnTaZo27CXecoKAMCaL3TesfZ2TmPHj3i0eMnKtmRErksSgeTiyKxbiHSxBq811mBUgXvIXeQU22bR6SY+6E6+pZv/ObTvJIAnXesho7Lsw3eeSWDTJGaFUZjxGKtV3SZ1QHESuPZQ7m8qII0ORTdDDR+NGIw9iUHWaWV9RoBZWzDvKmxeDvvdTDQeXLIyvadYZkW5mlCRsFZYegH+n7AGt0kStUSzH3YlPWmErFUW7Eh0A1rQtB5mBQjKRVi1D6P954QLNZ7sh90Xiwt6uyrwYnDuR5nO8R2RBwlo7Mp2VBQDrnThshR4FHwTQozU7EiXJyt+MyjR1xsVmzXHSIafe6CMB72zPPI/mBBnjAMHevNimE/cT1PxFxI6e7X1HlLqRZTlN1exFJFnXhORxRpm6nzhiCCWMdLOunGHVeOooyaLZmmfGsbiOmY4RzpO4/imYVCNbQhZd3Ujej4hTo1HWUx1mGcIbfhW5GX5+MlFvgljOcI7rlrKyk3IJMGoaWp2+pgveC7wObiTB3UiytSWchjwgwOu3GUAHON1AR+ElzxhGwYiiUPazbbM+yqY3W+wq091VWyFBIzVRLYiq2CnRVvX3Oh2kgik6cERfB9R+cs3WaF7bxSdBUlls5VB9uV+/+ot9ZEaT+B9Xy1El9KJwVMERWuMsUjVvVGxFoMhWQKncncvHiHt67/OkseELdhsEKZrpnjqHNT4jAW1kOPHxw3+2sO856YNUoauk7LLHPCWq1vhhA4uzjn7PyC9WarjXCrEd7SeOZio+dRqHk+OSjnBZcrKUMIllJ0doCyYOV+yGK/+Zu+Ce8cvukLaTRvCN7indLbxCWS4kxOSSlJjMMGpX2qopIZc6v/lwrUpicjWuZU0IlgjcU5BWQc+0+2gUeOQ7dHrRrj9DKUqtT+tRScKEfaNM6Mhz03O48YeHTxiPOzM4JXtdhS6suB3ju2ajziTOMNtPiuox/WBN+3jUCYJZJrJGVVJrW+ww1r/LBGUibHVlo2BnyH6VaIGxATlHUjZZacyUUdoZgGy9fqUztHcqKGKs4QrOXRxZbXX3vE48szLjYrSkksceKqs7x4VnnnrT3jYU+KC0PnGYaBvuuoeadzgPdAzeOsUJ2hZNv6t026MCeWedGMxjq8UzSk857Q9cAxO9Cf44C5afRERhSqbI8B1MlDoWW4k4PSa6k0sUgapFoqTTql7YxiqEaBQtp3uoV+lZfOERQNrFnb3Vte2rxo0tlOrUZkslWhUBscw3bFdj7j/OoMbMaPlvX5im7dIQ5KjkgCG8FnIVRLh5B9oDvfMpxtWJ2vMMESc9T5UgyxLuQaMbFi5wqxUlOlGgVCSMx0YgnrDr8eGDYDJjjqvJBLZsmVlC2YoqAiqwrmVl7Oa37c9moZ1LIQl3QaeAVLzqJyBaJ0+bYWOgtDgP1+Ynf9NtfP3+b66hnRQZqu2d28YJonTLhk7VYM645eera7FYdlz5J1mA1rdfPwmWWJdKGjHwa22zNC0DJgKoWUC3OMjNPMctyk23RryoUasw6WNjohawzVG7qKDsYVi5W7n9IH6HzAO0dwR3CIbRIXaOknawY5TTOH/Z5aBWcd/XpFN/QsKXOzO/Dlt9/mnWdXxFIUodfKhr7JNjvnlIrfO7z3+BBYr1ecn5/RN9Rf33lgTR7Ky/o4TQagZKyoJs+yjkxTz3rdYwxcXJ6z3a7xNqikRNaJTmfvfgsY1mu801LzEg+UFqRko8q4JVeKzh/gu4F+s2Zzfs6w2tANK3IphLhqyMUMxlPwmGqpRXRmLKWWucfGVKIIR5GqkujGqvqodkUI1rEKjs16xWY1sO4CQ+cwxlNKIBjBlMJ0s6OWxLQ/EKwlWEvnHGmeWaYF+QQktT/MRHS4OxVRdFfS2ZhpPjAdDtRS6PzAZrVhcGd4FxCrlY/S+qYnNeCqYoNHkUdzAjodc5oWKLWe1dHBabZTX/afOAYCtzSdjvMNtZ5K2NpbffkZR/ViqpzAMHdtMUagkpJnmRfmw6xy6qUwrMCGgPGW1dmKx597yua8J84j3drTdwp0ICf6WtkaQ18Fn+XEoJIOBxanFIiBjlAqrhoMTqH6pSJzxo1FGT6KIZbIUisrDMF4qmkyRkUoqWXAKDhuihlH1T3LFBo1pxZbP4H1fCUHlWM6iaiZ1t+Yl0SeMikLrqp0hbeVlQdbI/M4c9i/4Ob6GYuHPF+z39+QcmJd1hgv9KsO6wOb3ZbdeOAwzaRSsdXgsPgA07zQ9z19v2JYb7DOE5NmSykX5iVxmGZibIOUDemWc6ESNdJtqDdrTZtxsagwncHI/ehBTeNMdoniLJ3zYD3itUSXSeSiG+VhiVwfRiiC857qPeIDu8PEl99+xnd+9/fyPd/3ReZUtJnvTHN2juDUMR0HbldDz3q94snjxyoR4bQfMgRP5xxH13R06Dlmcs46eV8h58AyeNarDhFYrdeEoOXIOEey1d7aS/7ou7NhWDeNr0SMEyUr+OOIVlbHU0BU86Yb1nSrLd2wwnc9XgSfC3aemeelgfAU6Qg655NT0oHonE6SGEg9RfRacgpKQlwyvRM2Q2CzGlh1geB19KHzHmMCXoSyLNys1oyHHeNhbE5OKXDSPBHnhZdi6XdnBpUjMRXmZWE8jByWGw6HGw67K2ou9H6A8wt6C3ZY43xo8uCaKdUGsT8KE5ujAzn9AA00odRKSkl0fG5trz2lWfJS1+yop3VyZLXykkS2jVg0BOeRF4Sq85F3psN2y0rRayUXJWeep5mjcrDzVpncnaVfD1h5BMsa4qJaZjWR4wIZuloZjBDQe9JRkBRJYwHJeFcxORLwuFZ2lyrYYjBLxc21ZbGOORUkWTprMEF0uLwIdi6UOZ0G33KOxFjIkhEbdKTEmlOZ1/mPf1Tnld7RCfTBsx4G+hDIac9bz3fMz25wU2IFiDfaJ3E6eOtMJsWRw+GK6ATyhA2O3g90qwHThkV91zOsN6zWB1bjTCnCklTAqxzhzKuBvu9xVmd1jiSn2jdIpKQb6VGaIB/F37JKdXtncT7gradiyVbVVaUU7D2IlwF8x1//TqypOGvorCc4TwiBbgj0q46uszhbwXiG9RZrPV030K97XPDUcWGcMy+uDrz51hVjTGQRrNUZEycGbw3BO1ZDz9l2y+OLc1bdgLeOwQd65wlGS3pFmgx8a+aWUshHEtlWthGjN34IOvy7LDPTNLLMC6UWrLN0fcdqGICLO11PFQY8spHrhjDPC8scic3R5qwblCmGcc7IzYF5KXR9bH03Lf8qC0Ih5/qypyeqA1Wra1mupaIAAdMYNpxzeBcwUnFkhmA5WwXWfYdzQsmZOE1KfGoMOSWsGDbrDbUU9vuZFAur1YactLyaloUY774MbY3hKNyUYmKaJw6HPfvDDeP+mmUaeR4Tz94MvP3GOdvtBZvNGcPmjGG1wfd9U3Y9Rte3YPdVbjkTXiIEaYCh1nI7US0drz/4Cgd3C/hObfhRjk6vPVTzCYNG65rdg7uHzXatatVdwAga8GSdLZ33OrfYrQacddh+0BkxDHUaqVOEZDAEjBScKLoRV+kGxzao8plIxRwm4py0utXua1rW6HG4xv8n7cdZjzUV63TfXfYFEyKZmRIMUioOg3fSSujaA69GyBVcFzi7PP/Y1+vVHJQxDMGzGjqCd4xL5cVuYn89EVIiOfDW0DWKfGuUgDSmA/v9lc6bmMpmu2Z7dk6/WrdygBLOigSs7+m6gbQUEOWOilm9fRc6vNcSQq2QWj8g5QbxhUbPb08S7kfm5ZzLrVKXQrGLEywRcrwnHgn4ru/+HiBjKASnukGr9Zrt2Zrzyy2b9cBq0PKc71b0/cCwXqtzAoo4Yq7MS2GcE4d5IdPgu0ZwxtAFRVnWUDEYvPUMXc86dHROIyxSbkwTTXqjOfuUE/M0M08zucG3XVOTFaMqscuSmOeFaZz15gue1Watpb47thMZaG5owgJxyc1RRZU3sQ4n7brLlWlJVCIVQ1cNPiiDhPO2MaDX9n2tXtfF4pw58U62UTOO0u3OGrx12kCmsO4s2yEwdAEnUHNkyYWS7EkpVyoMw4p5jjx7/ozd/sCSYH8YlcUizhzuASQhxij7d2PcPzLvW6OKAXNZOOyvuZ4jz9/5Mpv1GWdnl1xcPuH84gnrs3OG1RrvgzKXA0f/cTvDOf1+dCHHEp20jKo0gvnyEvAgxySfY/7ES6dWTp3Vl89vJUJNhsu9ZFDDZqAUDeKMMSeEY1oSUx0xRghdp4S33uNLxcdKiRP5UDBVATcnjS6pVFsJ1uKwp3UquZDjTE616V9JA5Io+761ih7GaK/biiM41dXzOeJywo2AJEqnTDe2Ct42Da4mslVQ4Jn1gfXZ9mNfr1dyUH2nddDOO2wbSYjVsssOmQ0lGXpfkGCVTsdGimSmZeRmf00/9GyGgX7YsL14jHUdGcezFwdi3vP8xQ37XQQ0SxDjKHViPx0U5ny7dFd5idhLKsfR9z256EV4GCcKC86LNh+NDmeG0BFC0Mg1NpRkzZDnj31xvxp7481n1LIAGWcNm82apwaVU95VlmVk3Ds2q4Hz83Odb/KeKtoPKYBvwJHXX/8M47SQS8FZwXvP2WbNxdmWy4sLLrYbzrdrzjdrzrZrtqsVDmE5zEw5aQlnWRiniXFSePmyLMzzrLpGTVnWuga+sJaKUeh50rkfjEJZz6fz0zDmXVqMSnKrQb9t4wXmxH4gpmohzjisDXTdmmFYEbpAFwKh00a/GKed32NhqJWEFBqsWbuI0j8ZaxQ1JrVJtBecAEXLMr2FdbB4K1A1u6tohq+1fdXdkrZhHKaF6+sb3nx2zWEcORwO5BRP/YC7NGO1cNl1HRcXHavVmkdly7I8Yp6uuXnxjGdvvsnzd97m5uqKd9458OL5O7zzzltst5ecXz7l4tFjzs4u2Gy2+E57rsaY08/Lct0P/XxpvazcaMoojZFcjhlYKwtydFSntOtd76MO6jQTaAS5BzZz1+mWq/LsXnvFxlJzYUkLVoRhvQbnVL0haZktT4k8LqSshNgiYIzC9K3T/U2MYJwHDDlXcmzjKkX7eaVAiZGcElhHNhpk1nY/YDyu8zgZ6GtBXCOSvp5IZKzzdMFR2jk79v9BAVn+E+g5v5KDGvqOLnicPYpDCOJ6shmYskeq46xYAhlnDNVmjLdUKcS84ItmP8N6y/nlU6xTDr5nz695cXXDzW5kWRak5AY31bReh3V10zVSqXkhx4XURNxOkzenwdOXEw6mZU3OCt5ZvLMtuwBjMolIlQWR+xnUvb6+xkhWWP0QKFIokpnjRLoaOQgMzlLOzuisZlLSBnBzKdSa6XvP0yeXiAhx0SzBO0vfdVxcnHNxfsb5dstq6Oi9Nt+tVfqeq6uJuCj8f5wmdvPE/jCybyU7nYFa9H2PhKdWdZB0o3FtvXV3sc4RqYR+YY73wL5tlDRY0EiRopLpgjAMjfJGlHmk6weG1ZrVakUIXrNU5zS6dUoVJdadBpqB1st66aCC1xEBc2RZqBlKhBLJSyEvKiNRUmaZIyarwqsztfVA5VQFqKLifbHA9WFmnpVNoFaFeN8HjOcY5Rtv8MYrLY90lLIiLhs2qxW971o/TbkQx8OOZVnY7XbsDwfGw4H5yUSKkc1mg2xWBBNO6NJTJlPrrZCmgSWO/atWGjzNDR0rJGg2oaPQau/2c1pEOYoVHsuLnKosd2mmEeAaZ06lci1D6shLilGFNlNomnpAFYILyLBmnkbGeWZJync4dIHBBLwxitK1ClarDnBtD81aqk8xMbf9oZZEoSDVag/VVLJ3YCwhBO0n1UxMC3OOuJzxVMRVsuh651RZlsQyzSzzxDJNH/t6vZKDWg9rgvMK7cwFg6EfNvhhy83Ncw4lMWGYpVIkUr0Q+oIdAqEzOK/9ptXmgkePXyMMa2IqPH9x4PpmzzRFnUHJEznNWpeNEUrGO1j3hmAiZb4hTY4oOiTmrJYB5mlkPOzZ7w9My0yMmYCi3o7lPaVJSdhSIB8waYcpB4K7H5BEmkdWq471euDy8Rln5xuGdSClmZsX10haWDsPs9I8pTgRDjucD4iz1JRZ94HPvf6Yxxdn2gwVoQuBoe/ZbtZ0XaBW1Rda5pHdMpNjUuczR6UymiLjMjPOC4dpYpwnYoytTKdRq7ENeWUE5xwhBEIA74NGg87hg8eFgO961Zu5Y+uHFd4arKmUoogpg1VZ6rDCuTZw23qfIXT4EHQMwSks3Fh5yWNoFAF2VGU1SCuDRmopyoLQAh5tegm5KnhiGfdM+xtMTUxOGJxhcYa+c/TBa2/mSKNU9EircYj1FDGMs0a7Qx8ICOEeSqYiLXsEVVoFjDiqk1Yu71j1G87Pz7l8dMGbb3yJL7/xBrvdyP5wQ0xZM/B5Yp4nHj96gkjByJbgXcs85SucTyWf+k58RUbU0IByOrj2X056arfh5LU5vFPm1JzTS0rjewBJ5HICaZRjZnNEHrZrTqHnEeudrn0IbLuBTYWrqyuWd97mZqcD9WebDY/EUntLL0KqGuw67wid3peCzv/Ny4ydptOokLYBiyppLwvGQhgCvV/TbTZIyvi4KOp1OVCTDo2XYMEaJQaeFm5eXHP1zjOunr39sa/Xq5X4hjXOBo7a9t46tpsNV+st79iBORcm6ZhEqGaBYBk2kC3kNEJdYaxTmpioQP4lKpfTNO6ZplkHKPN8GsBdUqaUhJPC4DKDWfDlAIsjmazqj02i4tgDOJKalpzI2VGyChZSKjWnNhw3I3FHyAe8LKzuh4qP+XDD5fmKp48v+NznX2e97lnSwu56Ii8jdVlIIZGmwHLYMTklHumGFV0/0FlLvxk436wbuglElLy16zq60FFKZbffcTMt3Oxv2B8OChpYotLxxEyKmdh4DJdcybWNDthyKsUc6+ZKbKsZmpJddoSuOzkpMVpKvYcKCiH0CqV3ioQ06FyS9z3DsD71MK3TuT1rvQYu1miGbpuEe2vYHyWhjtRNAgqgqJmCXmM118a8rddeXmbiNDIedtxcv8DWTO+bCmznEUJDt6kzzMf+CgLG4bue1Xqj5VRTuTzfUBHG5T70oJQs9gihB2kaZiDWELxnFTrWQ89mMxBCQMTwpR/8MtP0jMNhp4FOiuSo13SKB84vLkhnZ/TDgPfdiV0C4ORpam3oSZoD46j00XpVcupnvTzer0T4aW/6dk3lpVO7h8uzAUFqcw5NBNJq9mMbslRlczLiLJlCpGBDz7rrAWGcI289u+YwLXgXmYdCCAYR/3I+TNypzySiJMkiqoqdc1Y2Fa2S4ubIHFWIM6eFJS34lAjWE6xjYwRGQ9q3OdhYyKZSUmWZI7vdnsPhwPIJMJ28koPq+jViPLUqUq8LgYvtluubDc71pALRbJmNocoCwTNshd1ux+76bZxbUSvcXO/54g98kVorc1x4/uxN0rJjHHcsizJN5FJZkhJN1pqVlkZmVjawsROuCPEwkQjUqo2+9SqQG/Q3ZUX11QbntNY20tWMqyMl7/HlQF9mVqGy9ncPiQYYr5+z/qbP8fWffco3f+Pn8cHy5Te/TDwIQ+cRC+vQMXSeYA3eKJqyc5ZV1xF6nWHSTdbqfS3y0lmIUcqiZWQ37rg57DkclJmgFFRWogv4vrEAiCG3nkhpZRHdtHXjPv5YqxlU13V0R+dkFVJ8RFKO48ef8n+YWRfwQfXBQjCnhrIzyj1mvfaYjFUKH2Mbb6McWeVeNp/hVvNd2uBo1SHVXCMxzaRpoubcmA2UiirFiWXas9/tOex2WKnI0BGcUPHkCrFUyBrt5waBV+0iQ79a8+ix8OTJJeuh4zOPL0g5c3Wzu/P1lCMNkaktCBTKqZSOZgPO48yaronZmWpZ5szNzZ5l3jPGhZoX4rJn3D/n+Ysv8+jyMZePHnP56BHb7TnDaoVzoXEfvuwb0uahjkRStYHtjxntD7VWwqNRf91G9B0Fodp/7oNKIvhO5XSs6O7rBRscwQUdorcGsaYBQspJIftMLNl51sOKJ5ePefudK549u1IcQNGg1Pu+sfNoJaAWGMf5xKPovWO7UidX60twyhIT07Iwz5GYEuPuhhgT5+ePWK+3rLuAdZZpPrBMkRwLkUjNyi40LQupVlzoPvb1eiUHFfoV4gIFe5JrGIbKMKwJnUpdTMnRlw7fDzjvMK5yGCdi1An5eVp49uwZV9fX5BRZ0sw8jUzTgWm8YYk6s5QxzKlQalOXtIZhCARbidOO/X7HPoKEDa4/A/SkCFWpj2IixUhNiZyVcLYGS2cKhojNC0YSfTAMndCH+3FQ1giPLy/4/Nd9ls+9/hmMFdIyIyWz7XpMyaxDx2YYONvqMKnrO4b1lmG1JvQd3reMwAipJoV9NjbzUrUPZ7zD+oAfVgQcJmnPwzqPEW3OW2txxrUk9FY56QiZOvYDRHsTtvWyFOHWmKubPEdsBLN3bc6FRpR7dDraw1xKJBUItSrjiZNGCNv0y4zhyE0InPofpbG2l9bvKznqT9KySJxHas5YUQSUWNP6XropGGsbYCUoQXEIiPHkqs7QNNkCVaytIEYHp51qeG1XHY8uNsSUMObuS6Y6DKvO6XiHaIbShAyrqOaY8eAcZ2eX1FJ59uyKt958m/FwYJlnprxQ88gy3bDbPeewu+Lm5jm73RMuLh6xPbtgWK0IQQmkxXiOXH4vw4Rj6a4dR8uWjqXBrwSO16/MkI5xRsuc7sE3AeC7AEZ7es7rdWi93nvHgyqltgCxQIrkEtnNE9dYzroVq/WGR5eP2R1GRFTp1hqHEas91FYFyK33dCqdKsxZP6upSB/L2OZIOTUvyj4xVpbVhpASxWiGH0ygk8i0ZAVvKAIJ4w3WO1z4+AfJX7HEN2Ab+iOLVRVlF/BdR9f3jOOBcVoYgufy/AxvHMkVfD/hx0zKcHN9w243aoO/KCN2KZrxzKMKDfrgqFhirqqIGyw2KOqslpm3nl3z/HrP8/3E5vwpl0/Ah4GYCvvdjhfPnnGYJqYlARrtp26m5oAfLMZVvDF0LjD0QvDa/LsPe/r0KV/3dV/P13/95zk/uwCpfOa1z3K2PafmhEXonaPvAkPX40PAeI/znQ5EOtuarZodxlhYUiSIwUkbMLWO1eYMsR3DJjJNC/OSQAQfOs2a6jFatu2/nIAqOWdy0fQ+J0Wg1VqIqTAtCzEtynCe9fGcklK55Luf2zGNL66UyhhnLVksWkcXsQzDijNgJUo/JI3Y7Tg7UxsvYaGQS9ZB6Zra/FSk5JlatC9aU1KpjKLD39Z4umDBC87pAganDmoVAkPfGMxRrjkw5Ea3VdqsUam1gXp05oRa2O1uTmwed21KS/SStdxIy6CAE5U+cnKwzns22zPOzy84Oz/n5uaaw37XWK8zpUSWeGCaD1xfP+fZO2+xPTvn/PySs/Nztttz1uutIitDr/B0peCnVJpcx7GEp9nGS8SenNzS8f9P7FDajGrgiHvyTqDVht5hg6O2+UdrhFpUUggE1ytAgpoRlOfuehqph0jewtn6jKevvYbvO8bxQJwnBaWVghGDs65luE0KviiEJOfK4TC2aohri/Sy1Ge9ZfA9bs6kWMnLxO4GUoqUlBA0S6vLgVgjxRqchc35RlGmn0CP9NVKfEPf+oyGJAYrgFU129Vq4LAPxDkxx0JFOdGcKXSrhWGBWiy73Y551v7SUc20op7eilFWg2GFKYabZSSlii2ik8tDoEbD4TAxpcqSlGF9vz9gxsi8RK6vr9nv94zzwhKzNup9oZoF4xY66VgHw2Bh5Q2roIgquadB3W/4xm/myWc+w7DeUqvKGoSwwboBY5U2xxuLP87WtHmcIpYlW73npKLs3YlpjsxxwSeDtUr+uqRGzxMLMQq16vxNFaFWQ4yapmsBR8+BUzK7pqsUWaLCzZc5UhtUOmel/FkaT2CtRbOHUrmvLUAbzAu1JlKc1GnmptzqAi6oBEYqCV8ytFJSOpaSSiG3oCmXTK6JXFILphLUiNSMOWYVzuhsilEpE0X0mRM7fWio0e5IM2W1OV5KaVLwrf9UOfVJVIEWHTxPifEQG7z97lfVHElX67HPq329224Ajug4nRfr+4FhtWK1WmmTXgC0GkLRHkZcVN9tPOzZ7a64fvGMzXbLZnPGZrNls9kwDJuTo3K+R4uoL8lhfZDmvI5qBUc870vQ+QkJeDzeKl/hrO7afAgMq57QeXLJWi4XSDEy7feUKoSiJcCaF90Pg+eQM3lecMZjbGC1WvHZ1Yqb6xdcv3hBTdpTDj5grH5XEa0oSKvF5qLAqFIazVNj7CgU7YV5leipVZCaqXlhSZlxXnQMQuCQZq4OO25qpHqV3OjXgRRXCl//mO0VHVQgpkKpSmtfq95Mzns2mzX73Z6b+ZolZqakTNLOn9FvIFfH7nrP7mrPzc2OlBJPnz5mGM7IWel8uuBxXc+wveCwVF6MhWkpGmViML7HGKFbZ87tQNgUchXGceZwuOJmt+cwTm12JxJToe8dTiqdLAxm5swlLnvPOkDnlHm91qRR8T3Yj/tJfxvb86eMEcZ50oylVDA67W1Fh5inBBJBVz43SpijPHulimY58zwRY8LaTEHYHQ5M83JiUahR5xacObJxZHaHPVc3N9pzMpau6wld0GNJiXnRQd1xPDBPc6O7yqe+DOR2Mzjlj7Meb7Weftd2c3PDsoykOFNK0tp7CAz9QOh7Qqd1cs2Ikmaftaqce4yNOis2CiOdqRKUN9aY2mr89hT5eqNZTkmplUx0DYNzL+Uk4KQ7pfxzWgJMzSHWVjI7siyUUhqAZSYtEylOiqI0d1+GVj2n1iSrWUtl5pYDuG210ec4aQjPDucaTP+HOINCLYm4FHKaGXfXPH/H4Xyg73tWKy1lbdZnrDdnrNfnON/jXCB0K4Z+jRODcV4d+kuk+mloP7c/vBsNKFWgmJeq1Hdo3nm60NMPPSQdnym1Mi8z435HzqpMYJxQS6TvNStPppLJvH24IZfK51/7LI8vHmGolLiwu7pimmecDxRUCt55w6oPKutijO4Py6KD1tbpec2ZKU7MaaER8+ueHiwpFfKSKDEyThPX44EX04Hn88QkGdcZbLB4L6zOej4JtoNXclDeaRqfis7D1DZL4Kxh6AeGvmdn9sRclELGaApYa4frzhlWjrTAze7QONH0Zs5RsfndMLDenHF2fkkXC1eHyBITaZmZJuX9CyJY39ObHtMJ+3HmMO643h24ut41qY1KLoqI6voV65Vl64TzLnHRG86DMHhwRm+6IyjjPuzs0VOWbHnn2aEBPCoZRUiZI0VMlSYdwAndpKwPHCcQQbTslpLqD6lIY2G33zFOU3vvArkpuvqgw3Ypsdvvub650RvDWpwfG5pIwRJxmVnmhWWaiVEpj0SOah8q5e2MIEVILYsQD/Ye5kx2u2tyVnh513mddRoGVsOK1XqFDzocWWtlWRZi0sBkyYmYYyM3LQ3wcIuRw5pGH3UU2WvDkoJmV5JPwYVIVbjFcSM44sjq8TerDfucTllbadloipFpmlgm1eJSRWNl8nf27qGmreuDyma0C+6Ygdx+XoOjU3VYOabIsuhs3ThO1BJxTsuFtdbWv7Qvr10gS+tdTiOHww3u+rmiMrsVXRgwNuhwdb9msznj/EzLiMOqo+sUFKRjAo31Q1RF27RMVU65lbxsRt2xua6V5sWQlpmYFpJUpnliiSpuWfZyUl+g9XWtNZjBcdgt5OsX9L7DGYetsF6vSUsDQziPcUF7ok5Rqces0hhL1w0nQdec0mnEIWcU8m+PmJIjJ6IiOEvJ3Ox3vDjsOVBJBmKOmAgSDNYHLp48+vjX65WebDPZqJCVVNv2xoozopDjoHDRlCuHKZJLxZiKdwZv16w2AWs8Nzc7bnZ7lcMolSUmci5stoHVasPF+SV9KlztZvb7kRf7HYf9wmHvkU6HQ731UC2HuTDFwn5a2B2WBks1FLFYH+jXZ2w3nktreNTNnPeOMy940yDBCSha9roPc92Km0Pk2dWoqqxF+xEZoR41q6uoaujpVY2b7CvE2GorWxyHmyGmxGHcMU8juahqK+h4QB+600U6jRP7w6J9EWsQkxBryCVrNtHmMkpKlFK0+e8UrSXHTVsMkpseFJkklSbic6e2213jnKMfes7OLzk7O2O13tD1PSF4Pb6cyCkyz1PjbWyCgbVgrOBb07prg90nRnhraGdEARO1tKHd1Bjcv7JUfKTWobStPdPYLEwru6RTqXRZZqZx4nA4nOSz52VmasrGIoK/BzZzc4RC16Z1VV9+v5eFXEV2gihvY0rM06To3d2Om5sbSonKutGE+YL3BF8xtsH9jW3ovUpKC3McSTtl3y5ZyU5LEUCDzs32nCdPnvD06WMeXZ5xcbFltVozDAOhG/Bh0PJW492EdnuU2iD95l6QEr5XAcFa4HAYmacDycKcF1LNjWbsQOl1bk8Qcsr4PuA7x81+5ubmGlKhLomnjy7ZbtakuCDG4MOADz0hBKyp1Bzb9QneOYa+V/YIEcZclHcvVkrSGUeyUNqAcGl7yVEw9rAs7OeFHDoKotnVHCm2cP6k4/Lp4499vV6Ri6+SpFCkQMmtXKFRkbP2JONQM1jnGVZr+qFjnnbs9zeUZWSZRpYCuMCcCteHGaqjGzrWZ0/Ynj9hGNaYmDjbbrnYHYjTSFkOXF/dMCqJAksxTFm43o08v9pzMybGhEYgtWJ8R2cCSzHMRUghULyQnGV2QqJQsrCUTK7mZeP3ju0L3/3dLDErZ1a79xXGa9pNpGptUo2m36dZJ068aMgR/m1ODf9aFcWTF4MUh6kCosLNFcOiXFHK2dX6ilJVMTfXovQyDbxSU2oKoOUUgdasRRSVs4BsGrGos4pKEttKQXdrZxcXKkLYBYbVWqGvRkETMaYWDRbNmtusXMkJcUaHZ33ja7QGb6WpPDcQSs7kkikpk+JCjAtLXLQUlxacc6zXK/qu03vjmPU2LMFReoL2+eN84DAdtIQ6z8zTxDxNjNPENM8s88yyLKSYMCKkexgsU6CMQLEtf1LgQT2xa8gpq4fKNI5cvXjO9YtnTIcdJS0IlbiofpQO93o0rzFaNqyA00z1yATThOQboKS8nNfLlX0jq53GK3a7t3nnrRWb7eqkdrBebxlWW0UXB3VW3ncE73HmFo3VPcSkFWEcJ/KycNjtiGmm9o5SK1kgUygx4ZzOL8WoUjvLkuhDp7pYveP5tGN5Y2GKE595fIkxhvVmA+LaEL3HWqEkQ0pGQUuFE5BCgRKWapTmSPvdSlmkncbSKgJaXaoCoe/pgeQCWWARdJYvWPphg+uHj329XtFBCc5UCknrS6Jf0oiWdVyjv6lW6PuOs4sLnjx+xJtvvsH11TX7/ch8OBCr4LoVCcthzqzXG9Znl5xdvsb2/JKuD2AiZ+sth/OJaZrZXSVubq4pZaHkzD5WdnPmsGSmJbMsmaU6YtNQGoLH254xC7sIq95zcJ7eNin0msjVMFX0xN1DfR/gO/7aX4JiqSj6TNNv/V2MUweFXqxG2iCppZVIjpBS26DVVuU6jNXsRusmWDxODNVYiqiLyrWclD1TO5elOajUSGJzi+6lHBFkuoEUlDU554oxBUPGiRIFS3V48XCksrtje/z0tQbtVsoWjFVUYc5t2LRF+81J1MYvGKyjCwEXvPaNWj0emq5RLo2FfGaZpjYaMTJOI8syk3NiGHotfYpoD44j+KEBII4gjJyJUYemd4cbDq23Fxt0fV4W1QqaZ3LO2DZ7lD8BSe0Ps1oU3HSkITp1c45Y7VYqq6jjPewPvPXmm1xfvSAtM94Yhi4Ql4Vxio1d22DFYkRVr6tTeXhrlGbKGgWYOBRRaZeo81elKqoyTeT9rCoJ+yveDpYQLM4ph+fZ2SXn55dstuds1lvW6y2r9ZbNsKHv1rjQY7wgd5/gk0th3u2Z93viuAdTccGekHQZBenkqmMgMWZ2+z1hnFkaWtpte26mK549f8GSF3JJfObxI9abszaWY3X0xBqy6AD00mRMcsxIW38QjDiMbT3kVgkpDUxSi5Js6/4g9MOKjQ8sxrCUoryWBobNimG7xd43zFysRyRyZGuQWpEiKgeQst5gKeKsp+sDTx4/4pu+6cdwcbHlbLvmjTe+yDtvv0XYnFErBB8YVgNn23MuLy45e/yEfrNSpmRZGDaVzZLZThqpz1LZ72/YjTuup8huLiwZUjXgVlhvkV4IYug61fepzjFby01DRh0WQ1dU5yYlbRrmqhvufdhf/2t/GWMc1niM9VjTOODEIrg2C2IapFYaE7Fy4TmrWk62MSNY5xHXtRq0x4o7lWZMKwFWqyWb2hzUERaeUtYSXpN8fzkGUJB2rmk1/dqGCZ0Fb1vtuvUXnNQmAV3aHNLdWhiGxvFmGhGmopRA2ZhPMzRyCzJ9hNI1cctcCqU5qGO/rWblSdtdX7G7vmZ/c8M4HloGtVBr4exsw3qlUjQlBGqD+NaWqaYjvVQr3R2mA+O8V4e0LKQYTwS9qqIcUaCsPUky3LXlUpECFKMlYKm32BwUYn5siOaUlNboMCII280GK4bzs3OevXjBsxfXyjgQI0vKR4B6I+nt6Vcbzi/O6IaBagwxJ6Zp5vr6mpSe03cdwzDoPSENnFIKy7IwTRljLH2/0HcrVYBu/cNaEnmZWIzXfhRgKRi5+57e3KTc58OOvneNnNgRSz7B+Dny6bV4xFqrdERxwQTLar3BbXvisrBLC29fvWC9XrPZVLounAA6HEdHrCd0lpISS1pIZcFE7XlKTZgWsOasHH3VHscGGtFsyUDFeoMXw5ILxlSG9UA39Gwvz7GDp9qPP8N/JQdVTKDIQhWF2gIaPeUGZ44LOS8E79huBl57esmP+YbP8dnXn/D660/5wvec870/8APsdwdyqXgf6PqeswYtdcOKbCwohSvV9kjYYIcZN0eN+pfKIc8cMoylksSCczjf6SS1C40pIGCMo4oQreVaHGM2uInWM4FSmlPNlXhPsxHf/z1/Ux2UDfjQqZNR2TulFqgvm7oi0pyUymy7ljVZq2m9dQHxA9YNWNthjTZjVb206Tg5OUlDlHJLgO+kpZU1vaeeNqHjLIaI1eFWa3HeYoNmfN4KwQrBCd6CtaqvZeUe5qCczniYW+lb20ZbRaqV2aq6rlr1ey/zrFlWQzFZa7DenKiPainEZeZmd8OL58+5fvGCcRwVgl5fMptP08S8NKn2qk5JHY/2mebpyEs3s6RFIfotY73NHp8bwsuJaeXJyOFwuPP1LKUgBaTcpgTWVS1SkZpbGa6hRBsAYrs9o/MBAVLOvPP8BW+8+RZffvPLPHv2TAOr5mRs6OjXWy4eP+Vzn/scZ2dn4AzzsnB9fY3zb7IsCWMtq2Gg67SPE+PCOI6MB81kRYSuU6DV+fklFxePGIZVUzNw0EpWkhdqrph7AEbNuz2Hm2viNLLePGJY9VjvkFgJRijGkJtagKoSGFarFYfDnmWZiCWDMwzbtepE7Sb2y8QhTiw5sgorQgjUlNXhoFUD5zuSGB0XWdqoSM2Kjijassm1kGrBFGlzWJUjS79IwdmKKYWaFjDqKNcXZ5w9viCbzCF9/Ewnr+SgZjqSiVRpcNNaKEdKEpRdoOsCF+dbXv/ME54+uWC76YGBfugoxkAY+NKX3+L5i2tSgZotskDcRV4cbpqQvA6F7seFF7uJt68X9odCnA2H2pP8GUjFBu3RiFFONesC1ndY70F8K1tBMYaIQ6rBFmWTtgi1ZpYaSRXSPTmoZboBMVjrMXaLMb32nwqkJZGjMl3neuRrVtSTOp2XmYKxVp2yX2HtCuM6xASQVh40NA4914ZKNbspLbqvOR/dErWl7loO096SMwbvHB5H7wyDM6x7lS7vPDgrWKk4KU0xNZ9IO+/axDT0Ui0nFVcNKLVUF1Mk50huHHGxcdwd50WM0RL1sB7o+oA0RKOWRI8bcTlB67U0Kyees3EcVewxJ0WrHjOj9vsSIzHF03yZCm421viYiHGh5NK4GjKpaH9qvAcHpT0zbpX0WkbZZpt04VpGag2bzZb6+mdJ8YmS6RpDLZUXNzc8/szbPPryl3jn7XdQZLj26bq+4+LsjCdPnvD6659he3aGWMsSI7v9jmFzwbA6wxjDMKhoqfeenJWIdt+AJaBVmadPP8Ojy8dsz87oQn9C8llpMh+tAnAfMPM4T5QUEYqiRJ3gnQahnRdyNtTY+m4547qe1dBhg2OeFdg0zzPBeDbbNdU43JwRa0hViQ8qCvYR6kkfUgQFQFlPbWwvJUVqisBt4chGCC00Em6DlEISVe31JSvdmnf0qxVhNVB9JdXIUu+Zi+9qESQ7jATlF6MgYqiiUWvfdZydbXn69JKv++xTHl1uVXrYWMQMXF4+5mquvHkzsX/rmnmaKWXC7SLWOJVnLwWLllnGWLgZZ15cK5EsqZBSIHUXSOfwWGwrI2nab8F5snUgDjA6syUqCUhVdgXXhoyrFCJOH5f76UF5qygyI2B9oRsEZz01FRYic0nkvFBS1AZ9TpqG11uZQANViHE4v8G6FcavwHXk2uh7Gs+bM16VhW0TMWzKs9Sq/GBWUOajJhnvDN7rYKSxQu8dZ71nu+rYrAf6zrZBZ2UJENEbT9Fad98zKTnpjVjl1Lg/4vNLqaS4aGnu2DtqTuOk2loLzlm2241Gnk4dcy0qKmiMJThP13Vts5MmGQEhBFJOKjOx358Ij2OD/mtf76WjW2JkmeemBq0OKzU5+VqqbhS5UJYGoJjvXrNMS7zSeCwL5ricoAEqWuIFvb5W6zV9P5yQ6Ea0NHg2jZw/ecrFkyc8f/5cAwcBqcobueoHttstFxdKeSTWknJmtZnphnPW28dQwQevCMAQtB+aM9M8M89Tm8PSyH7oV3Sdqvkq+a/B1KMcouqo3cdkSclJ9fQ6YdUHhuCwXlGxpXiKVDI6g5eKOp5utSIMPUNcmMYD02HC9ULXr3A4uh5C54FCypGcFX1KVWLaVGvL9NFsrThymlXmJSbt13qvopSonpmAUoJJhZKIphBqIpTC4C216whDB8EwlpmljMRPYJb0lRzUF770nI2HjYMORzA6U+NcZRgG4Jztuuf1zzzl8ZNLtmslL4wpsh8XvvT223z3932RL3zfG/zAl95imRZSLogoN1TJtQ3S6QUUq2XOhXGJ5CRIsQpvlEAWSzFeEWwNKlUxFCyU43S5Pf2dCrUINStnHMcBxKJ8aPWeNHWDF2IWMBkhYR1sNj1eLGkOqtM0NmTXMutgY6SV5FKjzDkGuEqA6lxgvfKEYc2YM3NOLFl55GLJpGgw2SrtZm2TOdJYyjE4YwlOJd2HEBiGjtVq4Gy94mzo2XaOwVmcE4RCysrcIBRM29C9N4Rw99xxKU5YCjjLUfunjedQS2Y6HLi+eqElk3lSdOJRXVUU/u2zYxiS6pCVihQtBxmEzgfSMFBrYYnLqU9Xa2JZFp1FaTQzpbaRgTY6oOCT3PoJqsEVl9jq/NoTVdRU1pGAogPAZZlJzXndtS3LginKllGkII114AhBrrfm8jRgQhGLWptumkaVmDNYq5RbzmtfqwJtaNxZh7Ed8wKxRAqprVml1J7V6jGlVKwVpbKqqvFlLHT9gPPtDasgxjJHw5Jyg8mjKFgxbT6tgBOqv/t7vu886+1jztY9T54+pls5Yp04xAN+ENhBNoV5KWQisRbVV7Oe3ljSElnyzGE/IjFzEdas1ysljraWEiMJnT01iFJy5UIshnyL95FaNWB1ro1ROGhjE4IiVzGQKxSrnOrWZHonGNdTup7qhURU7TriJ4KKeqUd5G/+wFu8frbiybbj3BvE29a8r8ox5YRgt3zmtac8utgyDAGhMM4zz1/s+N4f+BLf8Z3fw/d8/xu8885zbQQ3Vu1aWue1VK2PilBtoBjVxlF+OKcINAxFVJHLiM7hCEc0kagjOsGvbZOrrop6zi3tVY/VnFP77Hsw3xqSWQq5RJDManCsugGJgWUK7HvL/mDYHSqzKSwkljbToQqjbQZHAeR0LvP4YsX28hGHlNjPM/t5YlpiI5Bsa4wyVesgqg6jBm/ovWPoPKtOSWrXmzXbzYbz7YqzoWNtBVuzzqsskbRMZGIrHQacN3RBgTJ3bTlOxFoo+Sgv3urpbf5pnEaudzfsb25YpgkRDbKsa1miUZqiUio1V0rKlKjzSjUpKaw/RfAQl7kFC4oKPBLkmtMlVU9jrrEx60/zRJxn4riQl3gqQR4RfqXq56YUKctCTouWYO8Bxbe7vsFotwNTG/MB6nBLqe1aevcPzWkf0y1R+HSbe3S+40g5pPN9OlIxL4Vxnih1ao7p2OS3GNNTqaTcPmN5SVqs/ebGRF8q83LkjVQCgKPekqWJlVoQb5Du7h3UZrXis08u+cyTS548fYQNsJtecD15/CjUkIkSYZ+IsVBqJpZCMII1HicOKcI8z9Q5cnbZ03WBvusI1lFT1tmx46By0eH/lAupqlpBw7po9cl5vHME53RequQm1yFkdCwlm0QuESHjxeN8IHWe0VWWGpniRLUV5z7+KtQrOajv/uJbvHgReHrW8/WXW55uV2zFU1Nkd3ONl8zZozM2Q8A7D+JYsvDld/Z85/e+wXd//1t86a0brveRpViyCNVUnd05zltUjdJajtnmeo4Envqc3Eox6vFPgj2tz9DqqOYorNdgq/Jufjg5RX35NPR697ZME0stSmBUNUofjIV+RRCBVDAx4krCU0mmtnmoI+C3nP5bSeSyUOvC5fmKb/rGzzGst5QKu8Oe/eGgDqUN3B4rYCq6pzdvsMLgLIP3rLrAcIReewsscDiwS3OjAio6i9uGW13wbaAwEPqgdC53bCkuWmIUDwJGbzc990bLlV0I5L5TlJdV4T1rHcYr4CQ4RVfllNjd3HC4vibOcxucnZiXRRk7Sms0U089KSNHpVQlOi45seTEfOxBtaHemrMqoAZlPz8CJXLKTb+s9QarZgUnSPcd21/5q38ZJ05hO0WzylLLyzGFFiDVI6/bkb6pUfgc5/JyVbLSo0OjZUAiBme9yk1Yj3UesE0jq+EEpc34vct0JtCcWCOUCqohJ9tA7jG7o2owptU+RZ1KuPv1/LrPfZbPPrnk6aNzzs63GF/pZ0uYLHYECWCCYbeLzFOTtjHKM7rESo6RzgeC6/DWUJ3jkCLrrCMVpugwdY2ZYmsLbCql2lYhMMp5KkYzrVLxzrMaBkU1I5SSWFJiTpEpRxYKKUGcKzEvzGlPlEJa9SQjkLPyT34CbZJXclA/8PY11zfCbtfpQugOgKSF3W7Ppjf0nWfoe5WozpX9nPnBt674G9/zJb7wxXd4+/me/VSIxQJWXy+NJrPo1Ik6KC3PiLTyATrIqknQsRRYqMhJukBqa/aZI3DCNuJTQ1NKau9lVF+myst09x4apgBxWYi1kqnElJBS6QHpR1Y+YCrazIwLkiPSNsXauNHqbfBELeS8kPPEEODp+Yqvf/11Vl3PuN9zOOwZJxUky7mVkVp0eWQwd6Joos4q27ezlkIh1cihDZaO84FUEuIctgt0oVfexd7h+0DoAqHv6IaPf3Dvw6ykRLUKh0ea6CBQT0Plgncq6OgaWs87p9RSTTrEGUPNicOo8ts5Rp19mmfmpE7miHK0BsRKK5NK4z5S1FNs/aepKcouJ4b343B7wFurhLQ1UVJurPBtKLpt8q0gzX1cod/zhS/gjapon8Q+W/kyn8qb9XQd1lLb35vTbo4lt2xLHZSW3I7SD84G+tA3WqMOEfeyHHWrVAicmvk60HsMSC1GbOt1mwbOeTmjdRrcKujeQqFa4R5Q5pxfnLPdblivB7ohYL3gAtgAhILxYIOh6yfGQyYlQ62RZY4sY8RkizOe0DW2CGeJwilgsEWvSwUAwbxE5lKJ4pVrz+n66FykIdeKs44udFixbVQgMafEEjW4yqADv3NliokbSSw1YtYFjG+VKPOJsMS/koPKYjgsiWc3I4N70dLHM3qjw7HGBrphTT+sEOvYTQtfeuuaL/zAG3zX93yRN9++YVqyzhyJPUGn9Up7WSI4RT7SaElONW69IG2bohSFmqme0SmaEqxx+OBOUu/GCKbdQLkhBiqKPtQIQgfb7sNqq/sqr15hKRM3pZL9gdl6HZwr2jCdWi9Jm+5NL6Z9r+OlUUtkWQ68eOcNnn/pkm/oOh6fPyJQyd6xCKSs/T4lfS2tD1NJbUAw5UiKOjYwHqOoHJlKItasN3fn8L0lDAE/eGznscFhg8f1KsHySejDfOh6nli/pWXF+aQHVqoSa1oB35CPCgWP1EUz0NJmP+RYomrQe44yHMeExrRBT4POlZWqA9+xZRnlJfAh50TKud3IxwtZi9VF6v+fvT8Psm3P8ruwz/oNe+9zTg733jfUq/dq6m51Sy1hCEwIMckhLOEwGIHsYLCZPIDDJowdCgMyyNjIWBiIMGELyyCHw4GMsBnCIDAyCoYgFBgkGxtLTI3Q0JK6urqq3nDvzcxzzh5+w/If67dPZr2u4d3q+zJfVeW6kXFv3nPy5D77/H6/NX3X99tY4fMJJHGr4dPKg2JoqodwUE4c6y92mAMQDDThpGXhcnutWm+zqTtoilM/7tZhtKDyNOzfRNtrAbe647Y2WUuBK3pQTufFqQwors0POlYhvlVZF7V9VlNGG6y6OmtV37dd31zjZGapey7KJdvtQN95hn7D0/DURAtbT66LMykZEvxQK3nJxrKvQuwiZxcX9DHQO2N2WUpmaKCU4zyTcuYwTcxVqRLpuoGzzcbWf/SIdGRnnKkrYERwhnRdDGmKVnrfAcKcF8Yx8XyZWFJgd+FPoqDAiUrtddqrncoutDed+fDqgFTD0V9uPINzuLihG84I/ZaK52q/5+e/8T4///X3+foHz7naZ1Jtg6d3ma7Xxcba8G8HzBr5aIOkCm0BWoZkJcBgVB3uVo7c+TaR3kTA6urgWFtQAk03KIo184f+/g9TMLQcreSDCKUujDlT/ER25qBszqSyaGVpzqq0MsYJ6gvteUpaJq4+ep8PNxum0BGPI098IFQlt6b9qU9QK7kWUskca2asNsw3Lta3OqaJWQsLinYB6TyhG4ibSNz2xKHDD9HojfpI6CNxMOfk4/3f02kaEdVbclwtzWk1QtZSrPfZkHWmR1bRmih1sZ+rufX2dE3k8evwdCs7r4dkruboS25sE7mV53TVx2oyJNBYwWnXZT2BKmKN77Q0NJ8BIdb+jVWnmjTKQwB52ryYleQ4OQBVbEj/1GezDF6da/+WO/9va9OxBqPt/YG9djV2mlQTtXpj92oZWi75lOVbue8WkGHlRD19HCfqr9N1nn6dlbpSQpvKdhEh+/sv8T1/+ZLjpFztI0+nA0+eXPLs8px+E+lCz1l/dkfXyTOlxJwyJQslCzMVSkIlW6nS2+D8kheOOQEeV5RlnjmME1eHA0up+Dhwtt3ROYzSy/tTfw+RBvg5IVdg7VBrY6WplZyUcVy4Oe5ZaiAsl4RajX8159O4xuu0V3JQzgc0B1KtXB/NtZclMV0MfO7pDtftCP0ZEjYsRXhxfeDPfO3rfPDiBce0GHMuNjeyMkob31O9c+DCaTnfWT9OVii5u4WUOwcuGMtxG1h1zmZ8VNayQrlTJ1dyBTxECdZcHIwJuX+gDKrrI6kY15hljUKmoKWgzgY1LbY055Jb/d82/ikc/RartXBz9RHPBa4yHC8+5NJ3VoOvDX21vibKrJlRC1clcVUS1yVxUxJHKskp9B1+29PvOrqzLXEz4IcBHztcWGdLjGqm6zq6vjdhRX//Ier1i+eM0VitS26Dx8XKoiup6+q8ar2N/EUKkO0YbP3K28OuBUzUBsLhdKhaplRISyZnA1Y4B96fjsb2odzSJdWG2lsjzjV7sp7WGqS1v1uJD1kJWe/XUpoaGtaK5AJNx2p1EOaM61p6PrGe11aWX9kIrKdEy3pOpb52b+sKq3eCDVOv9zafAgyLV5uTV73dA7IWQW9LumsJkHXaoBaTt2j3P3tPegCU6YurK/TFkRArV4cb3preoMrbPOGMrrdS5W7Y3Q7Yu5FKZXNm+nq+S6SxsNQbrq4yQ7+lix1LziylshQHc2K/P/Dy5oYXVzekqmyGLW8+ecpZDES3o+t6XIh4MfXewzRZTquKC45hGHApsaRiPdQlMS2JcZkZp5kc25mNnbPLNDEdDq/9fr0a1ZGY1HvBMZfMzWjUOKiyHTYcs2MhsqhHU+Xl/sg3n7/kehwND+IEVrQetwcAGIJpTZjMGd3Ora+EqNKaoLR0HmcZVBv/X0V7wPlT/T4Xo2Ap1bpbim30znfWN+l6RPhUxLY+ifXbgWlOtyW6UyZZSbVQMCFHVZveKA0wctqwzVqS3YBjlWU8clA4amS6PlJ8B1gkBJAEJoGDVG4o3FB4qZlrLRxEGR3kLsAQ6fqAP9vQn+/oz3cmWx5Du/9iZVbfhA5jJIRgjsvf/wHw8vkHlkWLkFufbVUALqtjUBPPEzhl3cbTd8vVJ7I24QWpTdRt1eACKlbGqzUbW0S2Q9iJqZlq+xy0ZVjanFPJpZV17f+rapt9ao6rZRx3ET3rQfsQDmp//YLobmcHoUFyVBqSz9CntQEooIJrDkpXwEdzUFVO4oxrGfDk/FVPAan1g6uVs1qJtbRSn9a138q3lBDXv++WFu/uKV171u0e12FA++2neu++nY3jzPHwklIm9ocjx8kc0JKecXGxYdMbyWsfe85UwTnUCSEkQpdwHkZfSceZZUlUFlLtyBjZwFKt/3RYRl4sEy+WmVQKCzBMPXNZOKvDaU1ZNtz6hmL8uSuVlfMBKbDME/tx5OXxwH6eySgEy8AMNZmYx4npOL72+/VKJ4iqoM6jEikIixbcUrk6Ft5/eeTNq5HrsXA5F1wuXI0zV8eJOSsuBHwEza7BTY07y3mHU4erri3Ctshak9OtvFurE2qlDmeMqRScoaUy5KrW1PcNVFGqwStLm8MQh48DodvQDVti16Mqpr8z3rz2m/tJbLPdst9PwHTnPq+YvIrDSFyVFcmo3BZDb00/9l0tmbwsLOORTKSGAuqhlQiPorxE+ZDCC1d4KcohwOSFMkTctsNvN3RnOza7M/qzHf12Q+g7tGVG2qJYaZGv0SAZSGVluLhvu/rom6dAR1eHvfaLuM1M1r5FCCbfYv/XnnPqedbT37W2qP8UkqtN7bcBSBFP1/UMjdG51nyCoJtMSUPlVStprYftKoV+F6Z9V1hlZeOwmsL9d6E++PCr5qCcs/kyLNCjjWecsqiWQTnBZuS1nsqndx0UtKSnZTornoFWwoc1MGh7uBEarzN/33J/xCDq7Zv22t+agd7+1T4zB7ULdBcbNs+efqr37tuZq8J8XNjfXHF9dWB/M5KmxPF44O13nnJ5uaPverouMmzO8HGgCwOHZc9+PqAUcBXvE3kq1HJk1onqPMUZKWyNlWkjlGooWl0WFoHZFYoYRZWqNobzCs6YyrUWSl5IjVNRcXZWzDMvDwfev7riahnR84HNxRkhBOqSLABeEvFTQJm+WgbVtgniWdFzixYOCZ7fTHzzoz1f++Alvo9sNoEXh5Gr48SY7KhtU3KscsvSIm+nitdWcLLmiP3C0yEjd5yULcoTiSrtQC9t47f0fy0D1CYdoWK9qdD1xH6DhGipaZ6Z99ccrp+/7nv7iazr+0atz7d4mdYibkUlWlN9/fOdTdvGLqse1LKwdzOHas3tOSf2JfGcykdU3nfKdRQOXSDHCJuOcDYQzrf0Z1uG3RlDU6MNMSLeW9N+lUZw1k907rY5fXvg3H/EPx2ub0EGYhmShDsN9JNkiQVbIgXnmpCE3n4IJ8BOrSeHZWUlWTukBlDRjLAqiwY2mw21FOYpG39f09EyQEpb221dl1M/8NY5Ybeu/dV2XOvzoPfPbXjYX5kasDgcBVFtou+ArJL1K6q0nngMV+i5OamGwDXaCWBlil8zVb0tp7oV4NCIfk9ZWpOKqWumtaJ2W+CKuzPfuDqpNdFq6zV4ZIh0ZwPD00u2Ty/v81ba+0MpS2Hcz8z5wHE/skwL8zKBZKomzs7OOHPn7OKGbdcxuGiCoNLQzes9C2qD+yVTXSWJMx02HLhIjJHNpiPMCVkS0keKQK42NF5LIZWC70zJWAVqMQq4nDNZlXFZuD7c8PKw5/o4MrrK2dbOgzIvaM7IUtjFjt3Z+Wu/X69Yg2kLyXkbhnVCVcdSC9dT5msfvmT7J36O4zzx9uee8MHLPS+uD+zHiSlDqa45Ktt6Jh3hCSJNQdYb1LbpD609AtwKkvCtTt1UIhvM3DkDj6KWOViv77Zs4J3Dx0DoBobtzogTU2JMlmGMNy+Zrh7GQfloSq3OW7q8Vi+sOQ5CRZs2Tov97Qf1F7spbaUlBbIqkyova+b9mhg00qtyVSae54kPNfPSw03w1H6LP9/Sn+8YLrYM5xv61msKvSl3uiYRr2Cfn/dN5iPivUe8P0GBbzOB+zepycqYa5iOoKUdYM44DldYvdYM6o0+yn76BGhYncUtIsydIMy3RWrLgpzTpmDq8MGzEtDmxeQzaq0Ns2bnS2nIycqd4Va+9ffRnu/a+q3F2Kzv25w2YcoGN1jZOe5GVKJWPNf1/jTHhWJOZF0L0u6CwDp4+ItdSQOEtNcoDW+hrmW1dzXGGnhkLWufotVTWiaGsvSCxEjY9QyXO7ZvXNKvWmH3bWrku1qUeVwYjzPjwfSh+l5wwdDL3gei69nFnm3YGM+lA+EO92l0hGwVIoqaLI5XpBP6bUdfOrYFylyoU2KjngLMKdGps15cLagD8c5UJLwxzGitHCcTi/3o+gUvDzcsVHzf0+92OBe4/uiKPjjefvKUt99+k7feefu1365XA0nIqUd5ymBUCllhyoWPrg64r36dcZl4eTzwzQ8+4vowcpgWlgK1OrTe6heJ6kl11HuDsNYGa60YszSKQcpbObBNqcKK6mn/Er071V5PiB7vgw2adhu6YUPsN6g4g/am1MTiJqbp9ddPP5kZfUuM7tS4X/sjJ4fAuku/tWzxrXZb9qgCGThS+bAmvlpmSjE12Je68NJlrr0y9oG66ekvNmwuzthdnLO72DHstsTtgIsRCb6VcloG3Q5M5701WX3At76TD+bI5ERi+wAuqrEdaLVoXqtl3oqJPq7n45rhaS32t7TqwB1bnYVlh3ezG6M/anW/9nvbWm5lKePga6g81bZfzKGtgomn4fM7zun0u1nLeuDUouQ03/8aldrGPVZxq+agtLFc68lp6WnNrqW4U9ap1h5oB8dpHZkHu13Osn4Guva5bl/nNts61Wpv/VADSpw+PTWwhDkmj/Q9cTcwXOzYXOzYnm8BoY63ZfX7MnUKXnDB46MjTQs3VzPBC2eXPaGzqlIMHV3o6L3DhcAmbFqQGBAX8eHAlCdSrqRcG3u5ScJQ2yBNqbjc4CoqxGS99qU6pmr3VAGpFVfacHO22acpJ4555mYZ2aeJuRYk2hiJCNQlMd8c6LYdF7vIm2+c8fZbF6/9fr2Sg/LStHVqtcG41mBTqVTxHJZE+uZH3Oxv+OZHHzHOE/sxMc6JMRVqVlQdIVj6GatSTwdbi76CHcTqbyfNDQpppLR1zTBsTbabj8GCm+Kp1koIgRAjm75js93Sb8+IcaCKJ1WleEcOnhS8zcM8kGBhyQs+CN0QyOlOU7lFkpVTy+P2606p5GRr385Z9Jm1chT4Rk3UPPJSKt47jl6Zu4hue8JuYHO+5fJ8x+XZGbvtlu1mRxh6pOuo3pHar6uy4iEs0w0xmmPyERfiCRwRTs7KUJX3bWtpCWpzTg0Krc2h3M2AxOQN1uFuw9fcikOehr5Fbu/3ClCpBS0ZTRlxQnGhsZabttOq76SltIO3OaZcWglaT83ob2unzMBKe2WZmcbXj5L6XlZKBrHSpvWZsCxArK9nDqrN4omBwaUN3N+WSDn1rBy+zS3SnFODNp+SnhaS1bUcWleQHickY/s3sg7Y26bwYmW/qkp1AsEhm4H49JLNkwvOnpzTBY8uM+PNNfsXV/d+P7UXZPDEXcfZIISjZ//iyPEw8fWf/wjxjm4wmrA4eGIndMHTxw27bouPW2K/pZuu2I83TCmz+EqJNhiuHC04KjOaR/KYkLHik0NzZ6AcB1OteN/hfKSqo+TKUhPLPHI9jVyniX3NjB5y9MgmssFBDOTjRJWCLonuoufyScfZuRDC63f4r+agoNEMFTuoWJ2FbeBcxRBz+8yUZlOPTKZ7n5qsOafGM5RSSd6yKeNAayqwaiWRFVHlfGu4r9DyFdVW1QYgc2ZFaq0bP3rH0AW2Q8duu2F7tiPEjlSVacnkRZrEeQaF0D3AWDmAV0Lv2JSOlHKbo1kHQqVN7dOgtrb5P056YcdfyyWbg7LSECwhsg8e3wW6GNHY6Ih2A/1uYHfWc74dONv0bPqeoQ9Wal0jYVn9or22tAz2lFl4465z7XP0jY1h/Uzv3+506VqNbC3RsaJBuVO6cys1UcvSnb8FJtwpt90CJszR1ZLRbEwT5sA8qUvEbIO5d/tOqq0xXfSEQtPT1d658lMP6vYatRaWZWaZRtJy/xE/TejS0pM1k2/Xu+ai4jmV/0990hadO4dHGtPDGoiuJet6clDrelpHRGrVNoN+O/e0OnPLQO9Eaw3xW8ECNBeQvqffbYhnZ/RPLum2GxzCcpxYrq6Zrm+Yb+4fGDU3xeRUCt024qOjJKUsmcN+5qP3rxh2ncmB+LX/DDuFTbTS+q7bIgKdi0wpMabUqIlmBDWhy4pVtwrkZaZMC8dpIaeFOW7YDhtY0h0kpaIlk/LCVBKTVtj0bDaByy4Q58XOIdfQ2A6cO+fpm1uGswGCkB+azTyIUiigBa8WaapwGiS09Duw1EQ6TC2oV2qBWrRxiwlgkVhqMgeuob58o9aJzp1kt+0wrHZwBIcPkRAiipJzZZpng7iWClKb6Ks5p03fsRsGO4B3Ay50LMkOjckpWjNpnlEt9P0D1KMxPZhuExCJxGw9uFqsqdx4bU8DiTTJh1sHdXuQruS4K9zeOayxGj2l68j9hqHfsBm2DMOGYdszDJF+E+h7z9AZUawPdpBXNY7CAtRVRdcZSm+lEQJrarvgm/R8+xzD7b/v21ZqoBXAsWZEuFsn5Vby4RP7QHs/fuV8u82W1vGHdY6uVIVSECMnM7LXBrX3qbsVf1z7YFhwUdYM7o5H+jjgxXqmBpFfnVSthXkamafpQXpQvk0T4DF1WpU2KnLnPhmuz6xlRbfzZMbz2HUdXexPjOK1VhuaaAhVJ3KrU9ZEGteqwF1pmaKFlPKJcQNtDk2ELEr2ggwd3fmOzdOn7C4uGbZbROHw8prjiyvGF88p4wT59R+o38vG48RhPzJNM9unF/TbjpKUaT+Tl8zL50fwH1AoRsUkShE1PrxaGLoNMUTO+ws2cce8zDZUn44ckwXdxUe8eKRTclg46g0vD88Zpwk9wqY/Z+eEvGSWcSKnRM2NVcYJvg/4bc/2bMt2O7BZkjnVnClkY0HvIpvthu1lpNs5qlhv63Wb3E50P9qjPdqjPdqjfXbsYUSQHu3RHu3RHu3Rvoc9OqhHe7RHe7RH+0zao4N6tEd7tEd7tM+kPTqoR3u0R3u0R/tM2qODerRHe7RHe7TPpH3mHJSI/HIR+cMiciMi/6OHvp5H++4mIn9ARP627/DYl0RkLzYo812f+4Nij+vz0X7U7T73/KfioETkT4vI2C70myLyT4rI2Sf88d8C/AFVPVfVf+zTuL5Py36J7/uX8ns/kwe/qv6cqp6pPgDL6XexH9X1eR/2EHug/c7f8Gn+ju/xux/3fLPXvec/zQzqN6rqGfCfB3418Pd9wp/7MvCffKcHV8/8Gbbv930/2v3aj+r6vA/7UdsDP2rv997sUy/xqerXgN8P/Fnf67ki8m8BfynwO1tE8lMi8rtF5J8QkX9VRA7AXyoiP90iiJci8p+IyF915zXeEJF/RUSuReT/IyK/XUT+nU/tDX4He5X3DSAif4+I/MlWOvoZEfmv3nnst4nIP33n+6+IiIpIEJF/EPi13N6z39me8xe193/V/v6L7vz8H2j35Q+2n/lX2n37v9y5b1+58/zv+FrNfkJE/r32+L8sIs8+fp3f4T3/d0TkPxWRFyLyr4nIlz/JvXqd9qO6Pu/Dvo898EUR+RdF5AMR+ejOWv4JEfm32v992Nbpk/bY7wG+BPwr7TP5LZ/S2/me9rjnP4U9f1eL5nV9AX8a+A3t31/EIs7/Zfv+7wF+33f52T8A/G13vv/dwBXwF2MO9Rz4E8BvBTrgvwjcAL+8Pf+fbV9b4FcCXwX+nU/jfb7m9/3XAu+29/jXAwfg8+2x3wb803ee+xWMCCZ8h3v2DHgB/M0YndV/o33/xp3n/wngJ4BL4GeAPwb8hvb8fwr4J1/htb6Gbcod8C+s1/rdrhP4Te0afrq97t8H/MEfgM/pB3Z9fpbvLUb1+R8A/5u2jgbgL2mP/TLgLwN64C3g3wb+t9/ud/6gvN/2+OOe/x57/tP80PbAS+DPAP84sPmEP/vxG/+7gX/qzve/FvgG4O783z/TPlAPJNph0B777dyvg/q+3ve3ea0/AvzV3+di/ZuBf+9jr/eHgP/Wnef/T+889o8Cv//O978R+COv8Fr/8J3HfiWwtM/iuy3W3w/8rXd+zgFH4Muf5c/pB3l93sfX93tvgb8Q+GBdK9/jub8J+MMf+50P6aAe9/yntOc/TT2E36Sq/+Zreq2v3vn3u8BXdVWWM/szwHtYdBU+9vy7/74P+77et4j8LcD/GPuAAc6AN7/Pa3gXuyd3bb1Hq33zzr/Hb/P92uj9JK/11Y89Fvne1/5l4HeIyD965/+kve7Hf9+nYT+q6/M+7Pu5t18E/oyq5o8/ICJvA/8Y5vzPsYPtxS/5Kl+fPe75T2nPf+Zg5t/B7jLa/gLwRTkplwFWg/4aFoFl4At3Hvvip395vzRrddj/I/B3YGn0E+A/5pYu+oCVhFZ752Mv8XHG31/AFsNdW+/Rq9onea0vfuyxBHz4PV73q8B/T1Wf3PnaqOof/D6u8aHth3p93pN9FfjSd+hd/EPYPf6zVfUC+Jv4ViWtj6//z7w97vlPtud/UBzUXft/Yx/ebxGRKCK/DktP/1k1aOO/CPw2EdmKyK8A/pYHu9JPbjtswX0AICL/bb610fpHgP+C2IzBJfD3fuznvwn8+J3v/1Xgp0Tkb2hN1b8eS8N/3/dxbZ/ktf4mEfmVIrIF/gHg/6bfG2b6u4C/V0R+FYCIXIrIX/t9XN9nzX4Y1+d92L8HfB34h0VkJyKDiPzF7bFzWhlNRN4D/u6P/ezH1/8Pgj3u+U+w5+/dQYnIbxWR3//9/ryqLsBfBfzlmMf+x4G/RVX/aHvK34E1Ab8B/B6s/j//ki76Ndh3e9+q+jNYTfgPYQvvPwf8u3ce/zeAfw74D4F/n1+86H4H8Nc0ZMw/pqofAX8l8HcCH2GzO3+lqn6vCOfbXdsnea3fg/VivoE1t7/nAKuq/l7gHwH+WRG5xqLHv/xVr+9124/q+rwP+x57oGCO/JcBPwf8PAYcAPhfYBDuK+D/gTn5u/YPAX+fGGry7/o0rv37scc9/4te95X3/A+9HpSI/CPAO6r633zoa3m0R/u4Pa7PR3u072w/iCW+72oi8itE5M8Wsz8f+FuB3/vQ1/VojwaP6/PRHu1V7NNE8T2UnWNlk3eB97E0+l9+0Ct6tEe7tcf1+WiP9gnth77E92iP9miP9mg/mPZDV+J7tEd7tEd7tB8O+8w5KBH5i0Xkjze+qN/00NfzaN/d5LswSYvIrxWR/+yTPPcHxR7X56P9KNt97/dPS25jf+eryi0d/V5E/sbv8eP/APA71Sjb/6VP4/o+Lfslvu9fyu/9TB78qvr/VNVf/tDX8XH7UV2f92EPsQfEyEl/2afx2t/j9z7u9zv2aez3TwUkoUY9D9jNxLiYPikVyJf5DnIGIiJY36x+u8cf2n6J7/vR7sl+VNfnfdiP0h74UXqvD2WfqRKfiPxJbDp6pc7vxWji/0ER+XcxYsEfl+9OK/9jIvJvi1HY/5si8r+XO7T1n1UTkd8hIl8Vo77/90Xk19557HeLyG+/8/2vE5Gfb//+tnIDIvJXiUk9vGz38Kfv/PyfFpG/W0T+QxE5iMj/SUQ+JyK//859e3rn+d/xtZr9ajG5gBdigm3Dx6/z27xfJ7dyAx+JyD8vjbL/s2o/yuvzPkxEfpWI/Bsi8lxM/O+3tv//80XkD7X193UR+Z0i0rXH/u324/9B+0z++u/4Cz5D9rjfP+F+/35ZfF+BofdPc4dpGJus/8df4fl/AJss/1VYxvc5vjsV/B8C/teY1MFfAlxzhxX4vr6+j/f9NwFvtPf0d9ImtNtjvxv47Xee++uAn/8uv+unMLqdvwwjcfwtGM19d+f5/692L9/D4M7/P+DPxSQN/i3g73+F1/qPMW6uZ9g0/G//XtcJ/OZ2DV9ov/P/APwzPwCf0w/F+vys3VsMfv/1tvaH9v2vaY/9ecBf0O7nV4D/FPjNd35WgV/2g/Je2+OP+/0T7Pd7/+C+jw/6DwD/wJ3vvyMVPBZZZGB757F/+iEOgFd939/m518Af873uWD/Z8A/f+d7hxE9/ro7z/8b7zz+LwD/xJ3v/4fAv/QKr/Xfv/P4XwH8yU+wYP9T4NffeezzGOHk95RbeFyfPxhfr3JvMUf+hz/hc38z8HvvfP+Zc1Dfx88/7vdv8/WZKvF9F/u4nMF3ooJ/F3iuqsfv8LOfWRORv1NMafJKRF5ifG2vhXpfrSfyVV4D9f53eK2PU++/+wmu8cvA721lhJfYAi5YlPeDZj/06/Me7IvAn/x2D4gpF/8+EfmGGIfb/4rvf298Juxxv3+y/f6D4qDuThN/Nyr4rwPPxBh2V/vMyxm0+vP/BPjrgKdq1PtXvCbqfRER7D78kqn3v8NrfZx6/xc+wet+FfjL9Vup9wc12ewfNPuhXp/3ZF/F1F6/nf0TwB8FflJNbuO38q1yGz9Q9rjfP/l+/0FxUHftO1LBq+qfAf6/mJxBJyJ/IcaQ/Fm3c6z08wEQROR/DlzcefyPAH+FiDwTkXewEsdd+zj1/j8P/FdE5NeLSMRq3DPw/WgtfZLX+h+IyBda0/O3YizM38t+F/APiuniICJvichf/X1c32fNfhjX533Y7wPeEZHfLAY+OReRX9MeO8d6dXsxiZK//WM/+4Mmt/G43z/hfn8IuY3fJSK/6/v9ef3eVPB/IyYf/REmp/3P8RmQM/ge7/tfw+SQ/xiWMk98axr9e4D/AKvp/uv84gXxLXIDqvqfYU3Y/x0m+fAbgd+oJgXxSvYJX+v/2q7rZ9vXb//463wb+x3A/x3410XkBmug/prv/iOfvv2ors/7sO92b1X1BmvM/0YMMPDHgb+0Pfx3AX8DcIOJ/H18/f824P/c1v9f9ylc+ivb437/RfZ97fcfei4+EfnngD+qqn//Q1/Loz3ax+1xfT7ao31n+0Es8X1XE5FfLSI/0XD3/2Xgrwb+pQe+rEd7NOBxfT7ao72K/TDKbbyDKW6+galy/u2q+ocf9pIe7dFO9rg+H+3RPqH90Jf4Hu3RHu3RHu0H037oSnyP9miP9miP9sNhr1Ti+53/3V+v/TAQu4iIIjVDzuiSyVNBsyIu4EIkdgG0oGXBO0GAXAqlFGqtqFY0V2rOVIValVwhVSVXBedwoSMhHFLielq4GieOc2FeKl4CQ+yIzhG9Q0QQEZD2u3Km1IoTwTtH8B7vHM45vPM45yiqzDmz5EKphX/qD/7he5+tOPyXBq1eyVSOqZIyqDo8EJ3gUGqtlFJJBVQFcJQKpcJcYWl/JwVVqAriBJygQFZlKcpSlVyErFBEyeoppeOI40YVXMG5QqiKqJAUFoUiQnaBQ7zkpdvxvgrvL4kPjkfSMnOB8qbAe054JoWhJpRC0srv+jDf6z39r/3Nf41qGxNZ15q0dRB8++wloKqUkkEKzheQgriC8xkXKt57gveoClqEJRXSUsipUIp9RlodtQilghalVKWWAiiIUmuhlEwtlVIUrdr+XcjrHqjV9kNtH54KIHgBcR5UqXeqHD/zM3/8Xu/nW2++q0LFizI45Qufe8af96t+kp/+8ff4sffeYLcNzGlmSjAtnqVGFo1UAXUVyoSrM8FB5x1djMTojU1AFdUKIqgP4AMudIgLiAZyqhyPC9/44Dlf+/r7fPPDj7i6uma36fn822/ykz/xJX7Zj32Bs21kCILUjCv5FHUrFaWgoqgosjLn1ArF9tWf+1//rfd6P7/29T+lSvtTFVCkCl4CPgTyktnvr3n5/Dkfvv8+c1rIrtJ3HZ0f0BKoxeNF8EEI0RFiAPH44NkMHcOmpxt6XAyIU7z3dF3AiVBrpVLAFUQqilJzoeRMyYVaFO88wQdC6HDiSaXaOtZCqYlUjpSagErKhfGYoDic8/wFv/o3vNb7+UoOarfdsTvb0PeRkhfKImStLKrUmlEE5wBn+0zaIhQE7z0iNolWBbSCOlDvybkgKAg4JwRnh7CI4rWSpRIpUDMlZ5ZUCQ68OFwIeCcE58wBeY8TIXo7hIJ3BGcODG6pnWq1w4tacSjIw8z9aTEHnYFcIBWhquCAlBW02uFXaM7LHFBVoTQHsqg5qbQ+ht1HRFBR8vp4FXK1nysCpQq1KkUropXqC7kdhk6ljQMKGWEUz40Xrhy8nArXS+a4ZGoqZGmfXXAEqQSU9tHf//1szsk+Z9ohqO3/7eiqtZBzZp5HnFdiJ4RYQSpKptaCcxVQvA9IAJWKaqFqomqF6lDxgEPUmROptDetdjjWgtaM1opU0KqIKkLFY4eDOnC0jaEehwVargVTgpzekzzEGpXb6dFUK1PK7MeJ4zSTcqFUTy5KzpVcHAWPugEJHjxo9uQEtWa7PcEBEedo54EiImjwiA+ID3gXCS7SRUcMG+alcDhM7PcHjocDsQv0m55hu6HfboiDR0RxVXDeIUo7YCqCo1IQ2hZXhXbvhfsnne/ipn2e2m6sBYNO7IAXcYS5R1ykVM+chKSKFqWGQslCzhCjp1MhlYrMCdWM8w4tgqqnVI94W4chenQjeCfkVFCXIWacKxZIlUIp9eSg8ODFgiNxEIMnBIdqJReBtKA5kUsClKHrcESci6/9fr2ag9rt6PtACA4nAdGK5mwbSUAcBO9wXoBqi8aB944YAhZcKtUB6nABqJWUEjkVqtqyEeepFYtwa6GjECm4miklsaRCceb4zJW1je89Dos8V4fVBYsc1qOraqXUypIWcrYPxjm7voew/QzVO7IIcxUy7eCrSk0LtQhasMwnm2OqmJOpKiSEpMKiesdBCV4EETVHpJBVLENVoahSxZ6r1e5LALLaV1JBqkNxqDiK8ywS2Cvc5MSyZJgzQ1ZEHRupDGIMnx3g12RAH3bYf82o9e73KLlUpnnkcLih7x2xH/BB8FEseFFzUrkWOldxPhA726y4irhCyQVyQotDVajFvm7fswU/0pw/K6+YA+/sZ6RFbLaOHSIOhx1WIq0qQHO28DAeH1hdVKkwp8L+OLE/jkxLYliEtCTSoqQsaBBCGJCuQ6NDa6CmQJ6PzDnhaiBIxIdADA4nIL45Fucs0BGPx9n6lEp0MATH2SaynG05vzjjybMnnF2c0++2hCB4KUhxULKt6VpBC6ggavfRvhScBSzuIRxUNwDtNGrBjNAqPwK1Qux6xA3MqWMciwWWwTN7YZoSaVkYhp5+6E/BVq4J5+D8kBiG2Zydtyxrs+m4uNgRvCeniu8y3baAX0CSOaYkUB0OT2gpaNWKaEbE/kO1oJqpmil1JucZcPR9R3AdzvWv/X690qkcY4cIFhFCu/C2sZxtshhXB2UptcOZ02pZEerXj8SWfbVIFdS8N4LzVgIorBmXMETH0AXiUoFKVSXVSqiFUu1ArmIHN1JxIVp5D/Br9gRUHCrt+lURFIcQvX8d9/OV7aYEkEB1rjkPoeKopZJSpialVqEUy7CKWomutvuS1TKcpLcZlKpFSw4r5RVaWa85qopYMKC3h59rJRCqUCVA6BDXgY9kYEGZCyxlgZTpcwFVggjnzrN1Su8giOBw1AquPMgtPZkIaHNKLUU5BT45J1Ka8DFgpM0OtNrmKwulVkp1oJGgoQVgig+Jqq2Mp6BecGXN0D2CP2Xj3gneeVQc6tZVb+VXaZmStINJsKDPiZwc1hph12rloIeQmbJ9LrZWEFKu7I8zN4eJcZoZOmFZFlJScgLnIYaI6wakj6hGaghUFXI5slRBkpXcEMF7IahDxFsrYL0XSiuBFhyV4GHoI2fnWy6enHPx5JLN+Rlh6PEBPAWKQ6tHs5V1qeUUJKC2voXmvNppcN/m3e2Rq1RLpE7VHbueWpV5ztzczFxdz8ypEgJ4D+O4sCyFYVCGjSPnZPc/z4gUdttKjBOq1bKfXthtey4vZ0sScqEbKpsLJQyJEC3D1+Lw2hH9QO8FRwuStSCulWFraZWnRCmJXBaceJxXfLDk5HXbKzkoRUjJ+gvOYb0RVVTMqQTniMHhgwOx2qrUFiWhdmjGcHo1tFBLxQXBqwMK1cJ6vAgxRmJwSKkkEUY8o3oOC6QC9W59XuywtWiioqEgMRJUcSEgzkqGArhWSvReUDXn6R4ILnIlG4JEnDgy1ofLVUkpkxZHSRWtUIqYg8LKgScH1ZxPbpmRuVzw1UpH1QkFpTTnXRBqu2W1fcn6UypWpw492p/jhh0SB8o8M48TOU2wzAxZCVSiKAG48I7z4OikWKAiASmW1d63fRyVuronWi9PUFQzqqVlLwoUcslUErnMlDKhZERgWRwhekLwOC8oFeftgFsPU9SRiyAVUG/uxQnOeZxTnJ3ulhlh5bs1c2opUnNKwcp9d2jmVK1vVdV6V/dt0py7AiqOXOEwLtwcRg7jxKYXclpIGVISuq7gxRFih+8HRHpKFykVcqosKZGWmSVm5uDpgqfvAr2CdJ7g7Q7U1q/OOVFqBlFiH9k64ezygrMnF/RnG1wfcR7LGDRCLdRc0VzMQZUCVZFqQa3WauVaqdQHcFDr565Y9cGSpxY8FSvBjceJ6+trPvzoI168mBhnRwgDzvdM88ySEn1X6QcouZDSQs5HVDNdZxWhWjIihdgLm03P2e5ovb+6sNnB7jJwfinsLhTvLajsnMMHIHqCi5SaKWr7YF2WqtWcUzYnhauUuhCIiH/9VahXesWKGKCgLHQxICqoWGruGwghhHbYi5hjWuvm7V2e0mygVgEVvHeoOkqxjV+1ImLOLjqPlkIJnhSERQNzdoxLoWZlEwObLtJ7b4d8ypSltuzMNrdKi6CcnJql3jsgWOYg7pRl3bfdSIevHleEkott4lLJqZKTolmgWiM+V3NOSVq5UrBy3Vry47bEVNs2WB1ZZXVO0sobLWpbPx5aL4QIwxnl4hlle0byPdPhwFhAayHKQnUV7xUv0DvHZRc4D8IAhNZcOEWx92wfd1Drd7X1HbVawxeUEBziINdETQXKQq0zpS5AtkzcSevVeXxo7k6bg3LWmEYDWiPVOZxEhIA4y2Jt86/ZeusvtfLdWuYxgASWfSG0wPoEoLBU8BczhN6HrfvX/naUqhzn5VTm224EqcXKmzW33o/ixdGHHhcdtUTyUpjHmXnKLNPCPIF3EL1j6APbNDAMBqBw4tCipCVznBaWXFDn6TYb3BZ2l+cMuy0SA0lLO+gtuHLOIU7BWQalxYISUUXW0l8pUB5mfZ7u653e4vo/2taCOeaFZT6y3x+5vgEfK6ETUjLATcrKkjK1NsdWrGeUM4D1BFULPigxVq77NcNZ6AfYvnBcPvM8ecNzdhbYbXq63oIo7w1IVLVCgWqRF+qUkhfykkkpsSwJcQnnLAHw8fVXoV7NQQlMSyalBfHePK73uBCadsftJhTRb0HWIe4EmrB3rdajEgcOa5q6Si2ZUioqlvnYAeChCto7qo+o8xyOiWXObGLkvO+JISBgi18E7wJBvEVjtZzKK9rq/sF5vHhqu6fr4/dt1zUgWdGSyXMiL4VUKrVUu0dqTrzW1iNCyVgfT9dWr5hjUuR0it0ezG0jiLRyl0X9ayaprYzkVBE8Gjv82QX5jTc5bHZcq+NGhTFZ3tZpJTNSiuJVOAue8y5yHoQeIVqqAIvVxu/bzPmsB6pYxNwi51RW1JwhmLou4FwhpRlksS8yKhZ9rj0CBHK2HoylSRVRNeSV7y1j8B6pPd5tESIi3gI0d6dH2jKpdV+UFcFXqn0QajvIgjSltHJULZnSEIH3bc5u5grToKgwzQv7ceTmOHK2DQxxLW+qBZM5IUAIgdhFqnrmecbHgapHximhJUHNeFH6GNidbdhserquwzsHFZZUGOfZHFSMdF3H0EW25xeEoacAx3mhBMjqid4RnLdqSfBW7vNq2Wx7LyuKUhpA6jNhDV1nZVwags4TolLqws1+IXSBoWxQFzAO1w5Vb/mteEIMBipzsWW7rQyXCzkp86SIZISKD5XworK/EY4Hz+c+v2XTbQgh0vWGJhTxOOeheHJVimZKTixpJi2JZcqM0wJSqdXOFh8eGCSRSovu69orajXfYIeAU7UdCaeN6JxlWev/ixZDNGHQX3GWXgqOkiulKlmzZQfOFpdXoXcOJIAYVPjQzcxjonOeIXaEYB/WqAUt9oqndnW1kLTWBr9uJUkRKz/e5nT3by+ngi4FTZmSst2DUlE9ZdW41nPKqi0Twnrw6xMa2u6um9V21AGnAMGJmkNSy5ZUxNCWFJyCeI/GntpvmIcNVxL5cMlclcqhwuAjsR+AQsjQCZw7z65zDA662g40tWu1w/x+rd45dJxzJ2BBrQboKdnKE0pBfMFLRYs1jJUFkYKjsIJ8VOzQyLXitLbAC7xgEbtknFid3gmt8R8Q6U4owtWhoRWpt4GbZFrZ9Rb/YChNPZW4SsmUmqk5P0yJT/XUU1sP0iUVjkviuGQWhU3XE1yA5PDRSvWCGhTaBRyOGHu62OOcp6qwLJm8THiUFL2NfKSM9xPetfOgVKY5oU4gRoZhy+Z8x9nFJcNmi/NCLus5ohZwuogXZyMW0m6qdyuED9GKK8UyvfoAu15XuNad/2pl3LRkSiqAI8aB7facGCdqWawVESKxGwgh0neREIKtlzXLFkdoTiKXTE7Way2ptD6mAUeqFnIuHA4F5yvbrfLkQqnn4KPDBbGkQT3qA5INSZzSwrJMdp1TYRkzlUKt1ltbASCv017JQU3zTCoVcDgf8N5Dqah3qHprsrcTc51vcN5RXYPjYoctmu0AFWedPydUUbSzs7a0qH6pFpE5ca2cJLg+MsTAFD1Tn6C0De9a1BmUxZtXr2pNV1XD+leqOU0fCAjO30FSPVCJ7/nVaKF5sUWKVouAtDlSDPKtGFwcLAs8ReN3Gu6Ia+UMTo5XVqfUGtC3e6NlXg5EnUX5IVKGnilErrLy/nLk6zdHDvs9aR55Gj2x6wma6J1yBpw72IoSVXFFcbXYZ5IzWtO9309VC5YEQyEB0LKUPGfmcWJeRpSEj0q3Fboo4O5kTmJlIWd4ZbtlNVNV8b6hUr0niLPMUzLIQvAzMc54H3Bu7Rtqq3rZNYnHmvW6tp/0NEZQVA2FWQqlVrTkNkeVW3nygXpQagXjiprjUKXgWFygdgPx/Jw+RPyiqESDca4VlPY60Xu6rqPrO2KMLJNYSd85chXmpTDnkVoq4oQYIqqwpIyPkb4dgJcXTzk/v2TY9CCZUhfr62nE0ePpG1SftgkE8a5FTgXU5t2EO473gc2cU2IaLTuhCl235fz8Lba7SoiFYbPl/OKczXbHZujZDB0xegvoc6EUoeCJfYc4dwpucjZHVZLByEsreXpfESZyGTkchKvrwsV5Ri9tdqxKNXSl2DxrXSyoWKZMTZWaQJMFbloTIcz0/esn5X8lB7U/jiBCFzpzLi3ss7PR2YG5VkXE4bzDBaurK3YIBrV6nq6lv7UwKEr0gRqrNfW1otKcDDYjEMXq+p139CgbgXnOpJTtUkRwruL8Cidvh0BRUk5UVUP2KQ0aL+BaW/qBFutxXFoIjfXJWmQOK4ABPOawSis3rf0jAZuhcHI6DBr+6zRPU7/FQdnoov2slUJwdvS4IpQ+kvueA8LzeeaDm5EPnl8xjyOSF853Az56NirscJxR2FYlammD19agllKRWhC9/wNVWw9kfZftP9FSKYuVUZdpprLgS4UouBpAF1QWi7C14mhZqGCRuFY8agPkCB5nmD0BKKguKEeqGgLNop52j9umOIFRcqW2gMS+rB9bK7drX8tpmBW0zeo9BCxabAC5VusrdYHLy3Munz3j4o032D17g83lliCCjgu5OqoWbA5sRR4qPniGoWe325GWmVqthGpL0HqkOWWmaQaUvrM+dKmFKJ6uKMEHNv2GTb+hix25joYWFU/wkT7u6LqNIQBblLaeM1ULuUwWtIjDu5Zh37vdZk8izuYQc2aZF8ZxJKdsZ6XrCOGcGPZ43zEMPReXW55cnnN+tmW76eiiRxVSKeyPlWlRqngbM9FKqZFQKrUUtDmnnG0NWVK5xbsFZOZwyLx4OTEM1+g5uJ03xKEaqtCCPWl910jUSlcymrDRn3Hh6I/f4T1///ZKDupw2HN2dk7X96DYDFErn0lr+4FF9cF5fDAntZbXDIRrB6c6A0ZUaAtZCc6hPlBqJbWFVMVKJoo3yDo0NJYNhualMpWFqtIaWQoOKpXSGqilKkvOqKrVt52z13DOWAacexDEGcCyVCuvQXM+rTgpt01Um0daoQ16QgI5zOGu5UzX/t+g9c42vtgwKM6ypSKGuhTnEG/DoIriaiANPXOMXGvho/2Bj15e8/LFC8o80dUMmuiHjidSONfCUDJeMyrtUM0VKYprB9NDDEKe+kY0B6MCJ5j2+pCitZDTgkuCzxlcokrCYZ+Ht2XUQqtb5CfqsAags8y+lRGTTjaNXxZKSG3wtwfpEO9uh1K1knOyqDZDzYJW3+q1rC0vM6GVwM21PQSQ5/y8Z5pmUqo4L1xebnjvi+/w5R//Mu9++cu8+bknbDuhpNkClEUp1VCRpRZ8McRv8I7NZqCWC6QFmocuotVYXErO5KmSG8ODuIJNfrRPQS0Y8zi8WHBQFKgVHx1dHNhuztgO5+b8UPBWws61sqSReTFUoPW73QM5KFui1iIVVCspFaZpYTxOlFIIMbZxiECtNrYwDIHLy44339zy9PKM3dDRdQZMK1V5/8XIhy8XDodMyrdoXtTQpHHjqbWSc6FiQJ3NcM5mcOR8wzi94P0PDpS8J79V8BLoY48TT04LtVZCjEgUakmE4vGd4EfhcCikKbNPh9d+r17JQYXYEdrga8mlDbq1AcQ7MasdtusOt3KUazXfWguuVKQ11hRH0dKoYKwHZdRHrj0uFCpBhFit9i+oZVMhkGIglYgGa4wW54hVWar1c9Zmjm+DuN57wgl2Lg+WOa2mK2ODtLLPnQxJG5zB3e2RNXSkZUl6ckSuRYorxREhIiEQ1EomEh3VCbnRm7gWXVa5DRKqjyziOC6Z/dQiumVC8kKgMKSFnYNzKZxTCDWDFrJA0dpGBFbn8DDAk1OQtCK6xFBd0VuvMm8iw+gZJ2FMCaVaK6LNNtnh6Yni8Suwh0poqLzobCxB1UpyrpWNxdlQo6paFpHBuQFxg+Va0vogqojLiC9IwXqDzthPRMGptnGB1Zvq6Z09xCzEr/qzfpLr6xvGcSL4wJNnz/jSV77MF7/4Hm+89YzNbsBTyClRqjIvC/OU8eGcbhhxToh9sGH4GNlst3aPBYa+J6WZZZmZpolaoe+t9+ZbC0FEbL+KsXXkbGwypboWBNlajtETuoAPFnTWquC00fMsLOnINB/IeSYEj7iOGB9mOH91TtbJ0AaQqLdlUbFs2qpDbRBEhVqUaUpcy8w8FfrOszvr6LvQSsWloev0dL4pli2BELw5q1KVXBTxjhB7kEKtM8s8c3M1cz4sTLsR7bSVWg2wFn1EqeQ8M88w64xmtZJf/nhn7fXYqzFJbHbEEKEquUFKZYWUt8209nRqA03cHleK5kQx3hMrvTlvw7UFUi6t9m48Zd86UFoJVGrwdE4IDuPWC4Gh68giSB9wMVD8zFQVl5U6WzPUiSN20aDw3t9GTg3y+5DmGqVTu42WTcn62Pol3D3r1zLe2tlza1nQWfe+hoBse1zX4Ys5dx886oSFitKGF7ESalbIohSJzFWY5sJ4XEjLAloIrtKhbLVwnhfOyGy0Ilooog3Gro1Axj7tivW27ttOUO519MEbb1n0wYaxc+Z4GHh5DeXqSGWBWk49Pe8DXejofEf0wRyumoNqt9doocpCrtUAE76BfVQpWqg6kVPG+RkfJrxEEAMLgENCxdnIDuL1NCTtW9++FLVZHVdbL3fdZ/d/P//8X/Pn8MEHH3J9fUMIkSdPn/HFL3+JZ8/eYLsbCB40GW1YSplxnLm5yYjb0fXnhBgI0eGis0a6GngiBs92OzCNBw6Hg42stHgy52z+WBp3pg92aJfCsizMy4J0Ql1HAYLgO4f49Z4VqmS0FpaSmOYjx/GG43hNKQsxRnzYgbz+pv4nsY/TrqG2jtyaaTtPLpXjcWKaF7RCbpx3OY88lwVxwtAHPvf2jqdPtkzTwjwvTNPCkhTnfRvfsUVbRHBBiAE0GQ9lKYGiEfGR0O3QeWI5zkyHwniY0QzaVVy0ubau7xCUeXakKVGmynJMpDHZcz8F5phXclDeOUMTiRGZemfT37bBxOr3AtTW5E02CmcHqVpPSiJSrd2aCSR1LA6KE6o6qngkgFeLUmuu5CWT21Dvmik5Z6MOIp4QrMHvukgoSlgKeszGRNGckPP+9NUC2dY8a/2eB4KcWu9IrL/RkHfWW1qvsV2myp2ZpVsmDhs4tkFSCYHaCTUGZNNDDEbCVwz+v87bVF356aDiyc6RnGchMlfDsHnfsRl2iHPEmtiR6Z1Bp2uFVA13XbkFQxkAW08lqW/xqvdk282GEAIhBLpoDfkQPH2MDF1HmkdefARTusEfvDmWFVCC4PF46ejihiHGFunqiZbHem0LOSnUBGuPr/1tji4jGNhCSVSNiEZEjNRTgrdebDUaHpdtRgewiBqsJKvOPGJtCNgHSPbffe9ttruB8TgSQmC7O+fpsydstwMxBhyVuvZzVU+N+SUtLMvMsiw26MyKmLXyWowRwdCKOWWWmJBptp5MrXgfWAn7tAWquVZSaUPVa4kpdDY/1cc2SF0QpzinNohbE6mMzPlILhOlJJRMWBxh7u7/hkLrkTYwWQvhqxr35jRbNvr85ZH9YSEttqMQ3walm3NptFOx89QqXN8sTFNugAlYEVFOjNS4URMifs3csD5oNXJY7weK21JqomqHaiDEgX6zIXS2n3wM9iIq5L7QdSObvuKlx6mn86//fr7aHFSxzYdA33en6NSJGjoGo2asagidqtoGeaWx79pCrVUbN5xnqY7khOK9lVlaOc6JgRuyLAaTzJVSC2iwQUcnWHvLGftxI5p0QZFYKHIktxKYtrKXQcxBpRGqwgql+kUDnvdlaza0FvXuwuNXR9UqApyYB1YHJZzmFXzX4fuO0jly76l9h3rfuLMa40dtrXr1FHGoBPAdBKM0qmpMAaijG+As9PTDgKsLQ5kI2AG9VGP4ZgFKsfmS9mdFYWrjQLtvO9+d0Q8DfdfRdz1d39F1kaHv6GPkcLhhmg7E63DqR7r1JgM0CIT3ka4brMzUCDMNmZfJizcgQKmoNMSTRRH2MjW3j6lQdQGNiHYIEe8iQmfEmlVOiEtJt/Rbdrj4tjL4lr7afdvTp08YNgM5Z0KIBhfvbWbGNY8j2vqdDUGpWqk1k0si5YWUAuBOYB6wHik+EIPN3ThvvZZUCimX9r1rQCmFqkZoWqxfVWsgOkc/ePpNR9eHBmhp2ch633ym6GzOSReKZmopzMnjl9c/t/O9TFvv2y5PT1yYtSrjWLjeTxwOiY8+2rM/2gzYmk2KWL84VSU35YOXV46SlXEujHO2Ad2qWBvOBtFVlFIFVz2hyqlyrGroVnERkZ7qtiiFgqcS8WFDP+wIfWxlUQtyBUfOynabEAJaKjHYnNrrtldyUNfX12yHjm7oCT6cGsRgiJi1qSwNwYR4nAt4p60MZf2lJBbYjzgmAhoCOJCgBO/pht7Kbzmh45FUCqVmlpKRXHHOSiC+TayUNlOSXWXJlTlXplQYU2FwghdvGR0KZT3o20Q/awntYXpR9vtpqLFb17Q6rI8/VxtCqa69KTFgSXWB6j0LsJh6hEmKSDCeLHFICEgfLbgYOsKwoxsukH5D7Qe6w8h0dcNxKqS5EKeZaTyQDleUnJg7z9ELVSOhRMTNuGUh5orUzC15TMvxHsBBvfXmW/R9Rxc7uq4jdpEYg0Xx3rHkycYRil2pya8Iig2I56osJdOHQu0MUu69s96rN3CJakWyAXfWcYq7ZRtEjb+M0taVcZghGchWhhJFvcd58Gpr2VeTWbGhXT29njFO8CBcfF1ne7FkxYdA8JHQda1ULqi2fhB2yIoYeWjXRUI0p64taF3HktbcweRIbKA7Zcu8anPQpyY/txl/rUb0nLMxGYQSELFeVYw2XGolXtoohiBFEA/OWw1VqzF455qYl/He7+eprHdnzdRaORxnPvjwJS+ej0xz4cWLmeMxkZZWPs2ZeVlwXqm5jc1UZU8lzQspG8CktCBUSjHKo+Dw6tAqaCnUDLkoS674xVvmiq1xlQ5kw1yUmyMMQ6XrK72rOBfszI/Wx9qIR8Rzdv6klcZtWPh12yu94tXLK4a3ntGFYBGQ2kCYFwyx1+YOnPjW4GxOyuhKKVSqKAvC4h3JRzI9Ug0Z5UqFEHC7rfWJ8kQRmNNMSgslJRaF0HggA4ZIKyJossNlPy0cxpnDlJiWTOg8Ue0xKbeNPCtP3rKfPxRYwnOLvkPvFMbuXI4dUG3D3kmvqggijiye6jwqnglImExB7Ddo58AF6CJ+GIjnW/rzM/rzMzbnT9junuE3O+gH6kcfcfPN9ymHhX7KXN/suXn+ES/Twv76in3w+OjpvSfWQCeBXiLKTMhQapOZuFOCvG9789kzYtc1HsfWaPc280ZDb+ViyKlasc3pfBt+blpkuZC6Qs7VysfizGe0sQSClelWRJ8NPEsr3TQXbalUA7/YuivFXLhTNZgvsXH2GXNAp4ZOkwacWQl9S4u06wM4fO8DoVrgaaWgYH8Hj/dNXyi1oFyNPWK7DWy2A/0aebfLVl0RqKYNl1IyUMU8M08TS8oojVevSU/A7Z5df0duRMoxQS32HOdXUIzHwPrWInCNpd5FwSUQg/5RNbPk1z+3871MW3vD3WEFqqUyH2euXux58fxAzo7jIRst1JLIaWGajhz2NwZqwAKGWpSaFyax/l1FQOwemPSRw5c27uNuUakVI522z9Sqdl2M9vMaGaeFl9eJ4GdwjgvsMwmxaxUFz+ADfbc5zffZr37gHtThMMKb9mZKzqRsNy96h9t2Vmpy3npSgBJQ9Y2qJVmE7aCEDo0DXX+JD+fUDGUuLMfRDgp1dOIhdvi+J3QdEgN1CVQ8xUdwAVXXIjFjNh9L4uX+wIfXew7jTMpKjkrRNbPjtpTXaqmuWibi766YezR3KuvdyeJauQhOqOjG/tyCa1qpToQMDUZtr1aGDf7sjN3b77B79gZ9P9D1A67vCcNAPNsQNhvC0NP1O7p4joZI9gbVfdpv2FQhq+f6xUs++Pmf52f3N1z9ws/zksIhC10M7ELkjeGMGCvqDtTxgC6NukfMoa4R833a2W7XDs+AXyUcnFipg7X231BRVVrWElvZxRClqJxKVdp6S0vOxjXlm+NwAt6f5nxUVqzlSqS3fpxtA69l1jb7UxFcNbqo0FB+XrzRc+ntaEZpTqpi5Mv3bcZMIFa1UGmoyEaG678FY3grjOd6tpuBYWNIObcCk7T1QtUkdqZpYjweOR6PHMeRJSXrGYdA7DrLAGoxR+OsD0PrpeZsTmqZnUGnq1rRZlVNsAjOWgGtjLjKeSiVool6/8xRxhQRG18gNCorG8nogmc7RHL2jKPNyS3LwjQd0JeZlEaGjc16iQRE3clpW1ImiLekQGkI1tTAn+1J9ZTxO3LqKMvMMAx0XdccWaUsC/NxpiTrD4qIlcmV02iOOGeCmnftU1ier+Sglmzwzy4GxmVhSYmSMiLBombv8f62n7Jy6KViLMZVKsSA8wG32dKdPUE2z0jTwrSfGOeFPCfSlIg+453ViwuCOt9Qf54aOouwsEG+UjNTKRzmhavrkZdXe6YmU5ELZG839NbBG+uEaLVZLK0P0i+BW5SeiPGc3Wk4NTu51tNUkdreM2STMzoSjR0y7PCXT+nffIuzL36Zi3c+T7/bEfoNhIDEgOs7m95HSDgONZIWZaoLB3XE80uenF+y3Z2xf3nNeR+5/vov8HXx7OeJeUrE0HG53bEZztj2oTVhlZoTpdqAqY0W3P/97PveYPUnhKacNrENOjucN3E1rdaPEwLRBYILZDG4uff+FJU7L8xLJpeMCw6kUcu4VbRQQUJzRCYSo+vQNXr6DFUruR3SXozCy8bXPdZzig3VacPrTlZEpJUNH6ILFcJKVlmpuZHknrCagBrSUESIwXj0vB/oe3NOIbReUnPOousMWjZE3jwzzwamKKXgQqTreobNBicm5WFQadeqMqv+ljYmb8t0c23rTdYwQVBnJVkfIiF2OB8MfFIN6fcQYxB5ye1wtw+35EJaFkSVs01EFFJ2jKOtn5wX5mnPkm4Yx5ecnT1lu3tC123wrgNcG5No6yPndr9dO0MaglqNe7BqxXlv8PGyUBbPsoxGnRQjITjQiZEJqQkvlfOL4UTkbcwid2q1n7K9koOS2BO6nqHv0ZKJQRDZ0fUdu+1A8GLKuitVi1qvZFJhLhZxetfRxy3dcEa32SJ9jy5GMSNSSHnker8npURJC6KJIDYNvVRO0u0xBHCemoSlVKYCY1KWArn4RsQJU7aac+8c0cspY1Ex+PkpAnkIXi5WoMNduiLa/IIgRlrYykucSnwKqHNU7/HDBn9+QXj6lO7ZU/zTZ8Rnb8DlmxzCjutFyPPIkq3HUhv56FIyKRVzTikz5Uy37Tm/vOArP3HG5TtPON9skbzwwRff4xt/6h2+9tE3eHF9hMVUgHfdhm4XCJutzcalZAu55gZjf4j76W7nyKohubTxFrYJMPphwzBsGlDBuBu9eELfG6eeg34IBqzoIyEGSk2Niqgxdouemvim5dQ1JNuagWGAolbwXDkKaxtgVimE1pulaexUIoq/nYWR2+i4VJNUuf8bWo2dpapJuLdASWqFTAMsVLzzDMPQ2LQ9ijbOQ4dIPQEcpAE+ykrbJNbTCCGCeEI3MAwbtrvt6XEUQgx0fY84K18Z1+v6d/us16Bopf/C43xHjD19t6HvRkpJpDxbpv8AazTNRs2kbSB5PI4cbw4s40iQwqYTgvcMvTdtPWeEsUZ35Vm6gb4/Q0NtvbWOIJFcFuPdy4sxR9wZ8bGBdaOrck7wrqMLAZEEOpHmQl6UEL0J0nrFd4qPjthDjHLS+LsLJrMg7NNdk6/ooDaEaE1n7SPaR/phQ+yHNixWWeaZxELVQsVmmuYqTMXhXGDwG1y3JXYbYqNMmtSkDpxkalk4Ho5cXR847A94D0/Ot8Y7JQFxnuQ8LpgqZwGWJTFXZcqQqmvNa6FoYWocfASLvKKjlWOaTPdamCkP56DWhs1tFrdKNDhjgmi9tlPTGEACxAF3do5/9ibdm2/SvfEm/skTZHfB5AfmMXMzjdwcDtzc7FmOI2meyfNMSjPTvHCcFqY5MZXC2+98jh/78R/jvXe/yLbfEM/P0ZJ574tf5Otf+CLP5z3l6jnzPFMrbLoDfezY7s5weOZ4oExLywlu+zH3adr47ESs+VzVnNTKJiU4NsOW7eaMLg6UOpEXoRsMJht7MQn4ztF1gdgFQgzk6snqSCXZsHkbqtRWknMu4DB0n00stMiiiWM2MLGV62rFSYVgeRPOCGuL2hc+nPpeq6RKqYWUH8Lp5wb60BP7d23jCkWbTPiSEYShH8gOlgylFOZ5xmWFRvyac27zfgJtgHydc+q6noAQu4HNdst2t6VqJdeCQ1pf0XjmajXhSVXbx6rS1KHvlPBbL8b7SIwDfV/oh8mId2sBzQ+B4WEZZ0SFmgspZY77A/uXN4yHPVpyQx07QlBCkJNQoZaGmJRVHNPCnuB7fNjgsp15eRnJaWql0bXWbHOSwXu8RLro2PShMZ4vpLQ3lGO2HpTbBJzr8GEgRGP1qDWR84xzVnU6UcWtnyfwcVDX67BXclDOe5alcBwXtJjWfWgN6XWotoozuDeOVGHJiVSUgifEgTjsiHHAiyfPM3XM5MM1Oh+IZHZDhIsLRKL1VUTZnJ3T97FRHLXyTQwm85EzGWXJhSVncjE2CkNktYakg1Trqnfa5h2t7JCLQaMfohwFa+Md1nxZMHLYu5/50pyUZVYGOQnbM8KTp8jTN8gXTxl9x7KfKNNHZH9FCT2zCh9dXfPy+oqrly9ZjkekZDwV74SUM4fjxJwrC8LubNvAGo5clCiBYXPOG+98kS//1K/gm/tr3n/+klKuSbnw8nA0ZI+PnItQJSK+I5RMR6F7AOBJzuXkEGqprLCN1hpHqjJ0Pee7M56cX3J9o4w3RwS1+37WmWSD90j1NoNUsaHdLqJzplTjGqQaZ59zxlF26pGojS1b07qNMLdqAjQJDSnIijEPpve15IxWTxBzULlAKspSKksuzOn+myZN9MPeU6M1821oNs0jaV7QpeAksOkHZldZamJJC3lKlKZlVqtlkn3f03ed9eRqa9b7wLAJhBAZhg1x6I3GrGRC11goOusT5pyarpswaJtvxNkgUc2n/p1VIgwa3QWBXhoZs2kqIYlVyvw+7bg/UlLBO8dxmtnv9+yvrjncHFimhaJCITIdFtAD223H595+l0oA6emHHX2/NQYZZ729EALOnePcxhL46sg6oVKst+kjXRfb2MXAZrezAKAeycsV47jANIJmkyNJhZyVcdpzfePpukrKR3Y3A33fE0NP38qwKwHCabb0NdurOSjnGeeFq5sjXYBtMCfhQqCUYs1jbL5GvIJY5GpOKxJ7w9XHrjckzzixzIl0vEHnkSjge4uUfBgsMqJy/uScoe+oag3FUgvR28LVZaGgZDUEVqm1iffRmstt7epaImvEqq1UZsScK7rnAWzNnuBbYLiru1L09H782ovzHWzPkcun5N0Fc+y5SYX94YY5G+u5hshUK++/eMFHz59z9eI5y3gk1MIQPduho6qyH0cyJrMhogybLT52pwg1xi3P3vo8X/yJAz/39W/wC1//JvOSOI4HDvOEPwZ8t2EMEadCkECPZ9Omo+7blpxP97GuWlB3ih1OhU3Xc3F2wRtPn1FzYhkn8lhJruJdRaSiuqrItnxW1CaTtOlJ6cpBaTM91lewnpcQWTMo1dx+913iXOvDGAijUr2gXm0IXh2xRgTHUsSE6ZpzWpYHYIdfM/jmpKQauKOmzHxzZDmOaFY2w45h2yHOSu2pyZosqZKy0Ul574ldNO6dArX1TxTjNOy7gd12R+wjmUKRgotW0vddsOH1soYcvjmhRvasyirLudbETU7HEIeuBaSlVI7HGZFkyt/3bMu4mMYKcHOz53q/53C953g4Mh0no3pz0frwbuJ8Fzk/25HqllI3eB9NCbxaabU2AU7nBgOouQ7Ti0oGyBGPc5HgB0LcNLmOAec6VJPVOipN4sfYN2ilu+BGUEfJmeNhZLPpGfqBoR/Ybnc8eaLszrb0w4rgfv32Sg4qiuP6+oAuM0/Ot4TYUbJS/FpjNup34xvzJmvR9Wy8R0XYnZ2x2WzwPhhv1DQx7w/k+YjmROwHfIxsujM2W8f2/Ax1sNv1ONfk5tvNKzmT5sXoQVyDUnp/4qDywUODV3qPqZsGo1gJ3lFKi7J1LRc8UM+Epoclxja+Rn+KwciLCrlBSFUcrtsgZ+eUs3NSN7CfZ672R/ZzYkoZ5yO+66l9R0Jscl5LQ6MZRVWqSvUdOI+rkd3mjN3lU9798pd49ytf4uLZMxBPWSpk4fzsCZ9/78t86Ss/wQcffsg4z6RkvZh5Xnh+/ZKjD8RS6GthK2LQ9wdo689pwa21mxbSrTBY37iKfB95cnmO6Lv0IdCHyJImSskcriZu9pnQC/0msDuLbHYB5wsqmZwXtJX4nMiJcNf7daGJTSo7bdFsG8aUcnJoNLBALplFUtMvsnKkFIjVxP9yEZakLEuxSsQDCECuqbzQWC9UcOohQTkuzNcjmisdHdFF8J7tToixMmwry3LroJz3bIct/TCQU6bqRMUxz00LrSjb1s80LSmQNjNWa6ILHT6aWrEpd7tGm2bqzjZJYFUTrYafXp2UBKHWwhxnumgHfejuf1DXOd/AHYn9/objfs+yzJS8UNJsg7YuE6RyuVWSego9+2PPYYxYHcgCdRuCnkEOONej1THPe5Y0Wv9PC5lCbuXVcZoM0eg9PgZqmSj5QF4OdrbSeA1DpesLh5vMzfXCzZVjt4PNptLHheD3nJ0dePvtzFtvV954K9D3nw6v4atlULlwXGbq7Bi6nlwsddcWampL5bUVhFXEGA7ocCGw2W7p+wFBKDURUDpdCXIMyBC6QNgMVN+xOd+hDmL0KNUkhjGJ+GmcOFYlNqXNGDMhpoa+8ninK/rhxHMVY2Az9ATnDP1TKjm3kstDMUk0uqVVLmNFbakzHa0q60dkmWndnMHFJVMcGEvlOiWux5l5Tmip9JstIUa0ZjJQaqGK4oIzp43Hd4G4GeiGgc1Tz+Wzt3jr8+/x4z/1k7zzhffYXpxDY0l2KnT9hrMnT3j65lu88fbnePLB+0zHI3k2/Z7xeGTxns57qvd03Q7V0kAC92u55BNsf+13rMKZ3jdSXS84NninBC/0XeDm5pr9/oYXhyuLsEclLQEvEL0YuauYZLxgczfeW5nFx4CLhqZ0OKQIxRkTUsmmr9MSrlO2bLRAhYVE9AFBTJCy8VCKVkpxpGw8laWUBwmibn/jitRx5qSqQFJ0KuQ5U/qCI9B3PRsXiF0lF+hTtbJ7e/PRR0NGOt/g3xEXgjmsXKg5Qy24aJ9NCDRJkgUJgaEfGkrTdkpajOR0mSe81xOyTeS283critqYvWNHwOiR7tuCE9K8MB32XD1/ztXNDbkUSspoyhbsREfXB4ZN5Lg4ro/GzLEsHnEJ7xIpHZjnYwNQVJzvrK9ebaSntEyoqvWf3GmuzCoJCNSSUV2op76qtM/FE0JmPBYO+8z+emGz6dhsBrro8QJnu5llEZDA7mzbBto/hfv1Kk8u02yy06XDEqWGtRfXBg4bT1PJlJSMMy+sk/yRrh9sQSIEETq1wd3xCCktxK7Dxw4fPTUGvApF1kIXuL6zdN55as6kGOiHge2usOTKcV4aOWUwlMy6qdVkNrq+Z7vb0ofAPGeO08ySJ/IDUh01BPGJv05FqaLGTRgC2g90/cacVRHydkveXfC8VF7sD0wNmee80IVG6TNEDgI1t6Y7Ns8igyI1MAwd28snPHn2jMs33uDdL36FL/3YT/D5L3yZN95+m37YGFxXV8ZzoUYj5O13G54+fUo6HjlePWeajswlk9WAKJvNwHBxwdY5NmW5/9upxt4g7ZDyTu6wQazRZ0Giw9ETwxMuL3a8fPGCj55/SHo/Mc5Hi+gXxRVPdAPoYmtEbctY78m36fqA61a6KE4wd6P+yZTGo6grYlMM21dqQUk46XB4sjSy5Kxoyah6tDhQY/8OD1CSWtWo7VCzuSLLpByBgKuOulRqUkSdaRfFnpQroVRKpLFnNz2iZJWTVZBzs7W+5+w8DqXkmZSELka6IPTBs9SFvBRc39PHJnBa7YwZy8w+QHAV0Qvj3aSpFbgVnl/J1QZzS8lW8vNijDX3bJ1XljQyXr/kww++yYcvrkiqRBc463rOz844e3pJ3/cowjc+2vPBi5dMY2KaEsqM1j3T9ILj8ZqcRgvK2gzqiQzb6DQaM0fTv2s4l7U0WGs5oRlFMDh+y7Ccd6Q5MB0j+2i0X8Owo+8Gog8ctkrOgegH3nzjkq4L+M0DS75HNeg2hSaypieIp7aBQlUr881pwWtH30Vc1xG73hyQj1a/rx4fO4OdFpMcd86kPILJ7bSSiJ4QKa5lGQ6TT+i7jjqYCJc/TqYQi22mUgupZEPGBFP27WJkGAY2XUeIhSqOw5SMiPaByGJ/EZRIpA0ze1LfIbszwvkFEnpqVpbYceh6DilxrJXU1ImjOIL3SO+t6d5YC7qu4/JJJMZIHzxdcOx2O56+8ZRnb77Fm597h3fe+yKff+8LbM8u6IbtqS7tVNBc2B+u+eDDb/Dio/cZb64ZnOfJsEEOHapH6/15pR8Gzp4+49k7n+dJCGzm6d5vp2+yGF7EdIMaka5r+j9aG09cXsglEaNjd3ZmwZUrzGWmamFZZkInbPsdm7BBMW2e1FpJTWbTHM4617Ie3qgNqHt3YklRG1xrApnWVCxaqbngXbEsy1Ujp6AxKiBN/tw+ixU+f59Wm3RFLQbXl6oU8UgVgkSCRNCFmpSSTNQwdibhoJoo1SokNnQLSjK2cmxQOsaI2wqdCGmZmeeRqgvit4TeM0SPJljyQmmlMB+8zVItC3OamKc9Ny+fs3/jKU+fPmV7fka/3RG6iMMkN1JOTPNESgviIERP193/cH6ejizjkelw4OrlNR+9uCJXZTsMDJfmHDbDQL8ZrNynMI4T41hIS0aZEQ5oGXHMOMl4MdXlXNf+p62UUnIrXTfg0F3KqFIw0dI2v9e0ftZqw0oB5nygixv64ZyznbIZhC5WqI4YJ66vJg77ibPzgWF4YAe16zqEYuWnanQxKRVCKCfHVBoNyTQtBAFXegYgrINg3lMWW/C+OSUhImSjnilN2lqzDeWXtaHcSnbOYJheYNOZ9Me8WCkppcWaySkzTjb850SJzqbSDSIf6fuerndUPFeHEV0WYwp4IFsb8lbGg+o8S+hY+gF2O/rLJ9APpKocMrzMlWXY0m3PyNOBZdxTqKgHpJCozJpREc7Pzzm/fMK7777LW2++wcX5GRcX51xePuHi8gnnl0/Y7mxDV7Wham1zPFor4zzx/je/yh/7mf+Yn/2Z/4Rv/umfY1OFc3UUdSzqmCoE3/Hk8hnvfP49vviVH+PSR8L4+hU2v5fF2GaavCc4+7IJeRvQTLWyLDPH4555PLLdDXTDJf0ucsEZuLc5320Zp5FaC5tNZNMFqiRqjcwz5qTU+H0qrGGpSWS0Zvxtw4nbZr60A9FZP5Cs2DBGAcw5Edvcj4MogaABV5ouVXmAEp8qS1ooS0GTUKrNabmqRNcT/QanMyXDNCdcVsI2mPCgQs7JFK+R0x/E1HNrtcy+6yKdc4yaeXlzYDwmxCfO2DIMPcULh7Qwj0eOztMNA+IgzRPH/TWH/UuW+cgbb77BG2+/yTvvvsfTN9/i7PyM0EcUZU4z03gk5dmQf2JKDPdtL5+/ZDqOHKeFw5Q4TjOpVLxzpFJalm0quzf7kev9xM0hsySDevtQ6bwwhHNkt0OctQTGMTHPuUlsVEpdKPmGebphGvcWYOiqLWWZ+jCcMfRniHfUkpmWA/M8NnFOex3vArvdMy4ulKG7QLt1NM1GDJY5cTwmliV/Km2SV3RQEcFISaMPjcrdqIZsM7pW6zQIc00OX7Kp4gZTiRyXmWUcKfPCEAND45BDIiVXConijpAWkmL8Y22QtmhjfHYWv9aqzNPMcRo5jEf2hyPH8cg4jYyTMV1EJwzRn5rkJ1im7xiKEvsexonlATY/cBodsDaeUMQ4Cicf2TtPwRFMs902KnBQhRghOPJy5FgyWjNTdYyihFKYloL4yOc+9yZf/spX+Omf/mnee+89zs/PONtu2W539MOGrh8Q8RRVlpRsPkShlsI4Hrh6/5v8wp/8E/zsf/Qf8f7P/ikO73/IMJwT8WwkcNZvkaEnPjnn8+98nnfffY+333mPMxfwx9evsPm9rPMrzZHRBq0OymhgzHHEaH2Q1AhuY+fpNmdstgObzYbz7c6od5bFGAek0MWISIej4lJlpcZdmba1FIOZn5gMGnawOSZxNrOzTt7KiuTL9aQgi/e4IGg1FowojlgdUk12pqYHQPFhPbB5niGJAThEiMUYOfp+w7BZcCGypELMhdB6Pd4HXFPGXemOpJWfalms7+FcQ0Fa+TClmbSMxE7oorAdOoIIUgslzSxzNKBTH3EKJSWO+z3XL54zTyPHw4GclWXJzG88od9uQCDlhePxQKXYwK9G/APMQaVsbOH4DnUBJeAkn7SvVNUYNg5Hvv7+R3zzgyOHw0jKESdKH2A7OKLvLODue5wP3NwcOR4XTEY+Mc97RBeWKaIhoKGJcQbXsqjM2dklFxdv4IMnpYnra8f1TbYeqzOnJc4x9B2boedsO3C2G+j6yKZvwAhRpmlimpZPpQr1Sg5qE6NJuIeO3bChi90d1VKHRLsJ07IYvDsXZF4oatQx8zhy/fKK4/WemhIX5xew3VHVoxLJZWRZZuo0Up3N/DhnczYo5vQaoCHXypIzV4eRD69u+OCDD3nx8iXX+5H9ZDT1pSoSXFOsXMXBaEALIz/sNxv8YSTr/rXf3E9iq+qlwfGF7IOV8Xzkwyocx5mqV9AvuC7iugG6wQ6OJXE4jlztD+SScF4Mou8885zYbs74iYsLvvilr/BTv/yX88677xK8b1LlxvycU4Oqtnq0VBNPrClx/cGHfO2P/wm+9jP/Gd/8o3+M+flLwnGE5JE4MMQNfrfjctOzffMp733pK3zunfe4vHjC0AAy920hhBMRcGiS7L45rRACw2bDZjPgoyNrYthuGM62bDcDwTtuXl5z1eTJl2nh+uaacTyy6U16xLuKmyvHlEzsEJruUIVSbEanOSddATCu0RhJm3VVI4wVMTJZo9oyxmgJVg70OKswaKWmQh5nlvH+S6bixMAcs5Xxcg2ocwgdfYj0ux3n4qmthJdSJuZshKKbgRA7Y5poDmqaZsZxNBj9Yr0TwbLJNf7OJXM87Ok7z8XZFlerORO1QMA7YYgdUhJzFxhixxwj82HkwzmTs3LYH3l2/QZn52f4aCMPS07WfxKh5g55gJjUb86IndJXx7A9Z7OdkVrYbmw+TFW52e95//kL/tjP/ile3BSO84YYzxiGwHYj7DamINz1PcPunK7f0A8D43HG+0DJicPR08VK56GUp/RdPEnPFFVySTx5+ibPnr5F7ALLMvHRR+/z0UcfABXvHbUUILAZnrHbvcnl5ZtsNmeE6Og6z6YPxM4zjRPjcSKn8r3e/ivbKzmorsHF1Te4dvC4EJAQLVJshz/iKLWS5sKEcnHZ5KDnxOFmz3Q4Wk20gvhAFZPJuN4fGecj6rTNLynORfpuABzTsrCkzJwScy5MKXF1OPL8as/z51fcXO+Z5mTT7SvEuEmiw+qY3Ik8MnSYg+o6ygPU96GV99SYIqr3aD/gLi8hdMwVbhSmKaN5IhalJ9IFSMvMOBpDxPE4kktBvKfrHTHaMOIwCNvdOU+ePuPi8hnb7XlrwldSajNlZYWXWdQkGAecVKXMC8vhSDlOyNwEC4cNm82WYXeBO9vhznb48y27p5e88c7bXFw8IYaId9YTu2+LwZRwg/d0IRJjR9f1razb4ZpG1vn1GSF6m9D2jth3bLcDqoVaEpuhsEwL02zDlCaM2dHrQNGJuXpSmwpYiWVFa9NzssP21J/CCJSrGLVR1VbGbkPaNtJbb3WptM1EZUFnKMfMMk5M0wP09IJVHyp6GlAuotZf85HOG7Apo+ToKWq8m7GTxu0W2rDorex7jJHgI9NkjCS1ZIpWfPDEPjIvjnmeOR4PLNM5Jde7BcI2e2ZfQRzbvkd3O47jzJIzNy9vqNWUeceLA91gYoa1FkIXEaD3gdTdP0jCdz0lm2oD3p8qTsHbfVGU/eHI+x9+yM997ascZ4cPzzg/74nBE4PgXKbqwpIqzKZmrZqIHfR9oBahlA7hnIvdji56trveCGGDSb6XWri4fMLTy2eE6FnSzMXlOU+fPQOs32/ECI6+u6DvzRE658klA4VUKsdj5fnzzNl5zzQ+ee3365UVdTVbhFJrQXyDkcfeCF2LIaSKCjlX9vPMtB+5uDjyxtNMmhN5STiUoe9PzcA0J8Z55qPnz9kfD4QhWuo4zgie7WaHimNcEjfjxPU4Mi6ZaUnc7I/c3BwZj5MR0jpHcP40nNnHaCSWNm9ukccQIQaCOPrNltgZYuYhbG2tVUBjhzs7Z/v2O0zdQBhn6nEmjZmSxPpMLqF+Zn9zzfXLFxwPe9K8GJw0OlxvTU1PZBh2DMMO7zumuXJ9MzKnxJKsUV1LNZaEECzCipZlmICnTf2fX1zwxhtv8s7nPk+aJiiV3eVTdk+eMbzxBv3FBf3Zljj0dF20ckHOiPdGrHrP1sf+JFa4GWygcLc7N6e62TTWZuXq5gXbsx3Prz7kZv+CpRa23tH1HdvdBgaY+8RHL14YSKDBpF3w+BpxySPFtYHwW6n7EyGVAGLgHFNDNX9UmvOHijStA2OaaKzoVSlLIY9KnRY4VsqYSctCeoASn28I3BADmjLSxhRc6BDf4fHEBviYxPpoc5rBizHNhM6yV2/UZF3XMwxbYuyJh4mUEsuykIDYd2zOzljSxHE/Mx5HDvujzVQ2tg7ToKuUkqg541TZ9Rt679kMM+M8G3XXfuTGeeqSGbY93juKZkIX0VzwKsYO8u5931BnskHZ9uGSM06N0MCFgIpwGCdeXF3x4YvnpBw4OwuoPsN5OxeXPDPPe+ZlBhdxYaCLHZt+wAdz5orNlL357ClPnpyz2fU4LyxLoRj1uQ3dDhvEwVAGhs3As2dvnqiLtJo47dDvcC6QcuFwHDle7RnH2WQ6guew79ntBt597/UHUK/koHItzMtCFmPZtRkbQX0ACWibPXLBoqrp+sDXn9+w3b3g8vwpvlb6bkB8ME0pZwO38zIzzxM5F4ILXJw/wXvPwd+QpoU8L0ypcD0vvDhOvNgfmVJhKYXxODGNCQpE1wb5gmeumaJK5yOdC40v2gToQmckq9rBpmB9qAdyUGg1+ZDdju3nPs/w+XeJX/gSVYXu/Q/R/Jx03JMXkx0QnxDvmUart8/HIyUvQGv84uniAFHYnRsqDwnWwD5MzNmy0FKsn+edR6ria0VzItVMEJM0GM7PePu9L7CMM84HxuOBlDLD+Tn9+SXx7Jyw3bSI2DV2+ErRwpLLg/AbvvXm24bU7DdN8mHHZrM1rrcW0TsPPnqD9LvK/njDvMzsj4LmBfGYJLlzxD6gTlhSYpwrPhqk3wXfuBNveSrgdlxBGryXlUW/6e5QFc2WbSG1sWubDEKpFVImT4l6rOSjIpOic21Ev/d/P70PdH1nysol4TTYuEg0VWDB4xu5ey+VRZSlJiSZs46hEHxHDRXfqMpUIYbIZmPsEk7sMAx9z/b83IbLa8apDZd2XU+MPT6YCKUqLNPMPE42rI/ShYDGJk1SbDzCq8NVEzNFtQ23zhwaNYumwk/+ivu9n0aJZbxAqxBhzYluMoWIUBzHcWSeE7W6xt1YSGlimg5Nw2xufZ/RFLDlxoKyoed4PBBCR83K+dk5m90Zw2ZHqZVxWtgfRuracumUcdLGsJEoJVNzPnH9lVrwXlrWJtZS2O/56KOP2N8cceIYuoGazjjsZ6bp9QPNXslBzSkzzTMZJaXZ3pRWinOI7xE1ddDQG+fTNBd+4ZvP2WzOeHL+jLee7NjtLvA1IdVURqfxyDQemOcJj2NzdsG7n/sCQ99zc/WCl89f8Pz5S6bjxH6cuTpMPD+MzNkogHIC1NGFYNlSF/HesV8mlpLpWrkniMF/vSghBLrtBkdgLELXD/AAiB4wBoscA/Hykic//hM8+fGfJL73JdJxZEiKvDyS64GlMTc473DBkeaZZZpI84TmjPMBiZEgji50hKFnd/GErt+YkOGSUT9b9lQLWpXgAzEaK/za09OSCd4RY8fm4oJ+c0YYtmzfeMb1zZ5xnvDDgHQd1QeDQ9upgOZsMugYLPg4378g3LvvfpHdppUhm86N9x1gEg04xXllu9nhgrAfb/jG+7/AOE/MeSS2gfE+dgQJBlUOwpRmyqjsVgHE4HDVGUuH3nVO1tg46Xyt5J5qMHTXsifVYp9l04kqtSAlw+LIUyYfC3KsuAVclpad3X/J1PtA3w9ogVlnXA10w0AIPdL03rTS5vAcNSfmNFqkviwE3xNCT2jkzka9Y6J3sYtNEsWQgrHvjVNOrCu7jAfmecG7wGZ7RtcPhBCpNTONI9PxwDJNBO/o2ihB9JGhM9aVTRwYos3t0MZV8pI5TkeWaWG8uX8QT2jzeM67EwBlPM6ICJeHIyE4DuNEKooPBvAQhTSP7G9e4EPABpQz85RIxRQEvLdB9Bh7hmHD2e6c7XaH8zYacfVyz8uXV7x4eYWijfdwS9dtbTg8Ly0waHIxWEm06yK1BLbbDYf9kecfXfHNX3ifm5u9VVjOLgi+Y5ozy/LADip4myMqwmn6/UTl0jTCtJU2RBxLKrx8uefPfPXrqMIX3nmLzz27YAjeYLRLRRT21yPH/UyeC52rMBdiFM7ihjrMjPFoQ7tzIk8zZVpaTyDiOyOn7cTRe09oyr4hQSqVmhM1eWq1tyriCD7Qdx3edZydKduzLf1meO0395NY9Z7u6RucffnHeftX/tlcfOXHmXbnhA+fE3cXhGGLi1et1owxXjc10rTMRmlSsukO5ZnjeEAOPRtxJv/sPOocWSvujqy2NKmRnAs319fsr15wffOS8bjHe89ut+PZ02c8OX+C9j2Xn3+PcHlknCZDG2Lch7kWKJmaQWuhZJNZONxc8/KjD+/9fr75xtv0nfWeDK3nbZZkGrm5uSalGQmKeEUlM8575jxxnG+odaF3jhyiybWUStIJiYWiC1orvUZEG9KpzVqtdlvouytEuVb7XOPeuxMIrU+vCqVlVsVmnvwd9vpbOZb7z6Cc98QYKbFSQkWLa3scmp5766EZ2tY5pQvegoFqyrW5CFULkl1jHIe+De2DUfN1MeDcBtkNbDc9Qx85Xl8xHW7oeush+mACkWlZmMaReZqNEIBIbQzwMTjERcQFNsNA9LENqFZyahkLtanRPsD9bKCdEA1pWopyHCdKWeiiMeiP40wp0MUtqemTzfPBFJ67nuCjCQ8630YPTMCxFJqDSQhHrq6ueP/9j9hud0xTZp6h1nBScZgmy8xqreSSTCqpFsMTNP5JpXB1dWAcE+M4ctjPLMlo7UrjAbSv0jSjXq+9Gkii69AGlOiiTb+vuiZW3lFjLyg2YJZSZn8YOU4z1/sDN/sDh/c+x8XZhiFGOnG4CvP1geVmQqeFUD3T1Z5eHcHD4Dt6H6zslDK6JHTJuM5ZWSl6BAgqBLH5EcvkFFcLNUEOjpq7xuPnTgwAoYskhLOzrfUdHsJcYHjjbS5+7Jfx7Kd+ms3nv8A8J8r1RNzs6DY7QtfhwoRJVVdKUzJOy9KGHk3ioebEfjySXaT6jjkVi+edNORjaeS4an26qkzLyPtf/wZ/+mf/BN98/+u8fPkc74WLJ094770v8KUvfJmnz95id3HJbtgSl4VlWVjSDClBEWOREJrTyszzxIsXz/naz//cvd/OJ0/faBRHFqhUVUpZ2B+u+fo3vsb+cAW+4jsh9MLzqw84zNccxxtSmhm8J4eOaRoppTCWPdqbzk4hs9RifG+qiO2AFphZqFvFpvJF5c6ALUCTAWlSUmvGpasaclU0K664BtN2VI9lfGv0x/0fqF4CIVRCUFzIlDaMX7U2jTKhNNohtIA0+XBXDOQhoBRyKaYQO1t5ebvd0g8bY44HYvB00eDjnG3ZbQb2Q8/LD21YuYtGFp1TYpknk4hfjObHB+OqlMbu3beeV9dF4+2shZwLuYkbgqK+8BDsMSJtHrOzjFKBaVmYpgJaGPpoUkFVrVRPMv22+UCtB2K/ox92hBBPIzO0bMfeTqQWYZpGXrx4joiw210SwhZVhw9964FqIyJebhklaraxCueNFqxVHa6v94g4UkocxwXU3oN3DUCjxYAu+aFRfF1HXZIR8JdKTZmajHIEmalVWaaR4/HI4XA0lE4pTMtiyA+B4zxxvtuw6fqTk5I5IdMC88x8nHEI4/5AP0TmkrgZR6acKZiIQa2lZUZNdthJY/wWCJ7oxZgmmsyzd5YtlKLkZANm1IoToY+efjCeqYew2m3wF0/5/7P351G27uteF/Z5fs37vrOpqlVrrb32PnuflnMRpVdpghEFRDREBGEoGCJeDBoHRkISEGLAgKImBHQkAxAGrXCVPg2KJAgagRiMwYjABW53ut2ttqpm9za/Ln88vzmr9rq7P3tV7XNuPWPMtWr2c/7m+/6e7vt8v+b4Luti2W4HnvY9u3HEOE/bdbgKPpCi0+FxGhj7HSGM5BwrtYk5ZEvFaImDiiKj0u5olKNKomILQ9/z9PEjvv97/jZ/86//dZ4+fcR2t8YYw3y54MmjRzx78owv/rDv4PNf/DK+7XBNS8oFQiTFgWkaCUGn/OPUs1ud8+zRI956/Wu88bWvXvt6enc5zS5iIAViimy2K958++s8efaQWDSLajrDZljx9OIpMQ1Krlksk9GSUIwTm3HFxJYkE1ISfVBNoxRUtBGz5yDTjMfss5wiChAsguw39KTqz/toU0FFSiprIpgkuKIoU5qi/j8r0a/kwg0kUHWg1amDsk6JTlMErAoumtpX2bO8G3UoWEMxyimZChUgNTL0PWEKFWKfKcYpzVcGrNPgwlratiPPF8SjoZLzovLnux19v2OcRgoFsQ7qRURHDIxTCYgimrmFEJhCJFRpeGMqDVC6ibK+HFCNShrQ0DYdYeoZhomYEtY4UqqqzimRYibGqjqcA7lMtSenJAfeWUC18ryvShFxy263JqXAer2m645xTsc+9kwSqbKE7Jnqc04IRSVR2o5cCqEk1iudcTJGs9Su6+hmM0XKegsVtp7SJw/i+YglPouRoBLLUySOk0oYT4FShJAiw27LZrNhtVozjSPempoKFs7OV/R9T9tqs3PWtMx9w0wMbS6YcaQ3hhgjq80a1zkihVXfczH0jBSCQCQTUqCMWcsMItgCjRgo2ry1zjBD5c29d4hATAryGIeJOAWaJmEFGm/pXgCP1Iex3MzI7Zwdlq++/YjwbMUuRlZDT0Fo2n393iprxhAYdxuGYVfhngoPt01L083p5kfMl8dKzNt1tWatB6WIQtoRbbyuV+d89fu/j+/5m9/N3/6bf53dbkOuUbBrPM+ePuHpkyeM04T1Lffuv8RycVxlqgP9MND3O1IYlJm+33D2+CGvf/UrvP3613n05uvXvp4H5qiapaScGcPEervi8dO3eeOtrzHEniLKWB7yQB92iNE6/pgEkyHliZBGptwz5VGHazOkISFRB2dFCrap0vKl0nLto/KC0hxVRo6SkgYXKZJKupI5gSS9mASuGBrnkVYwCYIEwhAgFcwnH6B+oAkWY4r6AOsIkjRrz5bGKqhJASNZM0AEJ3v2DkM2QsiJFJSUNE0D4zjiGuXDE2OxYslFkJyJBkV/llLn1uakaSDFoMHvdqV98JQUlt60WN+ogzIeY13tdUndd1Qnbgp72iXN+ihyI/RmqRQKghF7aDUs5nN6gZwmYiwkk0hZKj1XnZPMiZgGTRDKSAhVKbg7pm2WONfifUPbzsg5stkM9P2avl9hrWexuKPD+U1Lzln1uiphNpUfMhcNXr3Tdg4lEaaRzeop4zTRtnPmixOOju7QtHvtuEROIzG8GLb9j6aoW9FdOSbEQJwCYRwp3imNTIoMNYPabbdITpwsOo4wSNWMiinS70b6fqR3A1vvWDrH3FianCnO4ePENBTCmOlTYNUPbENgTIVNjAySiRXauo9cJRdsqezdZHx1TBzmcYQpJDabHd18w2xxhHGe4g1WMp2/IQflPBfDxOO3H/LoG28zGoufzbBdizStDpk6JYFUtdbEOA41WqklS9ewWJ5wfPc+yzt3WR6f0M0W3Llzh7ZtD47pUNIomXGYePbkEV/5ge/ljde/xnZ9oXMi3uoJMY2spolpHBWe3XaEaeL+/ahKoLueTXWUKU702zVnjx7y6I2v842v/QAXTx6z26yvfz1BBzpLIaXCMA5sNhvWmy27vmcYRy1riMqOhDTQT30Vd8tKMpkCKU/EMpLR40mKQ5KlDIU8FtIUcQ66pUPVm3QoKks+OCXVTlKKpZJ1cJxaGHSGQ60/l4Jki6nO0YlRAtoZdag0EXO6kR4UUAe7lQ1/P7gLoW7woshIREs+7Nmz1UFhwBYOYqEGBdXkHElpIhWn+lu5siiIZpKStSRojSFVUFbf79huN4SUtDdmmwO/pzEOnqsaaA+s7glVpkepPy+rCddt5aD6QGWoL3hvkdIi0hJiZJz2GYupEP0GaweMFQqXgAYFMcxw3jLrWubzJccnJ+QcgJ5xXLHbrbHGMOsaunbO/XsKitj1wjAEpilV1g9LKjoNevd0yb17d2m8YRx3jOMZu75HpxzmzOcti+VCgRqj9gLHaSTcNEhC65T1koQcEmEYyVbRTCEpyWaYRuI00XrLg9NjjG9wzrPrRza7Hf0wEaKWBqeSGHLC1rQ8W0MwhilHzsee837H+bZnSIliLFPKjKXosBmFkuVAzSNZJaJjThx1nc5oiXJ/lVK0J7bZ0jQty8VSI62Zx+ZE17wYPZMPsmQMq/WKR9/4Bt/36Iwe4d6D+9x98IA7Dx5coWay5KgM7SFc0sSI9fhmxvLklHsPXubk7n2OTk6VJPbkDrPZrNap1ay1pBjod1uePX3M61/7KmdPHiMlKwqy9RS0mTyMI6uLM974xtdV9Mw4pKhC6jhN9EOvzjL2rM7OePP1b/Dm17/GozffJPQbTL7+DWBKQTf0pCf6drPh/Pwpq/WaEDJGPNYVDA5jMiEE4lRUWyeN5NST80CWiUJUAlgxKsGYDHFXCLtM6APWF2LJdCVjkkU8YKqDqhmUybL3muqQjK9zehmxESOxMlFoxiRZsxXvDE4aRXAN2mvJNyC3UWrmbQ/yIlSGAaPS6VA3UQtFuTFLLtqHq6wapiQslbrJWpK1el7uOQv35FC5EEOpKvNFabfIB/n4Yejphx4QGmtxTlV4XQVIgLBH4u9lVq5eFJRQAUZhYhyvn20/50pikGCa9HPkFHHOMp/N6oxiJkpU8mdR157zRMyDgpxyqkz5hZgmIGEsNK3j6HhOLpH1ukGkME1DJVBOHC08r7x8Si6Z1drR9xPjmLBWRWJjikjJvPTSKQ8e3Ge5aBiGHQ8fvc6zsyfEOJDSgHMZ75WSKQaFu4/DqFqAn7B9pF05VD0m75xOhiOkKZCtAYtKrJeCc5bFYoZ3ltMjlQ8IKdPkTJtnRNeQSlGdFgPeGBpjmTcebx1ZYDuNPN1sebrdcdEPDFHRJdqHUqe0Z68AVHMmJRKFKasGzRQinTPMvMeZjpQywzCxWW9YPTvHGmHOEgcsu+un5QGYcmS923ARkxKYGke/2zFOevLs9a6MMYQpEKaJlJR803qP9R3NbE43W9DOFlincyLOOZ0Hms0OcGsR1UPq40S/27BeXbDZrAhxwjsHRogxHKiPqDxzm9WKh2++zt07d3WmqJuzJ0XNObLb7VSu4sljLs6eEacBK4X2Bpz+2cU5u92W3W7HZrNls16z2axYnT8jJUPjF7jSkCWCJKJkTB4Jfc+mH0hlS6anyAS2YI3DiqezghGPty3FJsYcGbcDU5zwg6FZOGwr4DK2olidOJw4rFjENBhNHyhFm9G2BnolAjFr5lBhblI0mCjeVaivEt1et5UrVQrnjKrQikK200GxWBBx2tsxeszoiVnIKUIuOhhrHDPfYhBM43UIWCwqtFGUd7OigCm59u00iBiGoQqWgnXKSLGXhtgHoJArG7ey3GhfTFFzV5m8tQdzMw4/RENMhTBlximy3e64uHiGd46m0UH52WyGa0BMQ85FJY4kEWJf9fc0W0850fcbzsUwDFvGaYdK2RdW6wuGYSCXhEUDIiphtgi0IYA4nC843+F9QwiBnAJN2x2qTwlIdQQlp8Bqfc7DR29yfnHOMPRMo+p4aX/vkx/M/2hzUOOANV7nbLwjY4ghUgyIg1wz665tscfHkCOGzHa9Y7Xa4p2mmoKq7aoKrvJrGRHapqUA23FkNwVWOx3MXQ0TQ0w1MhNlKTAazWn9XwgZZaoOiT4EQkqMIXDkLdJ1dI0n58wwjvoZvcda8I3F5sziBmhPAKYU2fU9u8o2bEQpWlJdE5w9cB5OYWIKk9aK92zd3Yx2NqeZzfC+rdDxiIjRPt9sxmw2q5yJVRSxZHa7LZvNinHsKTnjm5acE1MYL0siqKrpsNvxOL7NS/cfcHR8wp17qhm1Lx3utlsuzp9x/uwp280KSVF/6xtQLH34+CHnF+ecX5xzcb5iu92opHsIlFjwtiPjKErOgyPiSkeeLMM2MOWRbAawUXsvttBYQ9sIzjfYZo7ExE4GpqlnN42YMTNLDjdTPkpnDd45ikV7gNZhrM5TiRQtZ8VALrkiTSMlFCQqiSwmg8kYp1lC0za1B3H9jPtKZAtwKdIoRkU+S1FeTKV1UsCSAXVSde6IkiDqd7NZaKwqGtBYjFd+F006tX+F2euiVbRg1oxnHEftfYkcAAbOaq+pVBi5AhDsAcqds8WkSwd1tQ+7V+a+bgvRqNrDmBnGwLbf8ez8jNY7FosZi8Uxs9mcmXiMm1UHpeflbndBiKGCGRRS3vcbpmnAuYbN5oJp0jGR9Ur7/TlnilV2F80cVQ9LxBx6297r3mHtSIwq+JpyZtcPytIzxKpcEYhVrcJaRwgTRiyzdklKicwNO6hpmpi1lsa3+LYlGyGliTBOJEnYtqFrWppuhj05IY07wrAm9htMGmlFmDUeQyX0tKrdk4yyopeUmXKCqKqoTdMyj5lQHGJiFdei1uxroxO9HouQjT3QyYy5YGNk5ixYq5x/BYVmT6GSdRalY+laZjdU4ttNgTjTevNyMSe5Fus9e46CfYnPGlOzRtVosdbh245mfsT86ATfdgr5jYlsE13bcnR0hK+9tct6e2EaJ3Y77ccUkSom2QIF13iF5EalQ4ohaFaVMqvzM1bnT5kvlzRdq2udIrvNhs1qTRiV+NJbQ2sN/gaGn7/7b373oRQ0DCNxCuSUyDFTYqxwWNWCSnkixAmTLZ2bc9Sdsh2hjyiqTzLGeUzj8eKV/QSQLMyXDUVmjFHAxSryJpWlYi+Q2GCdDgob67UBI4oeFdGSXgw6j5WyZhq5ZAITe8FFEUPbNnW84AbkNnIdXNIpvAqYqDNaRtWC9+TNJtcEaM+qkWNVj93RX6yYtj3WO3ynhMbWOW3GaD20dvIqkKEobdp+lCInlW/3XmV7TEXmKjo1V2YUPUc0QNPyJHBwRPvAz1pL0zS4fP3n/DgpvHucCtOUmaZUy3ojT549JcTC0dFdlkfHLBZ3GENgs17TtKqp58JYwTb7lstEijoTGaaBEHqMGELc626pY16t1rz++hsMfWA2n6n8kXP4pqm6fpVuq2R2/Y4QBi4uVjx9+oynT86YpqAsMSnQ99uDo/eugw4KlpRu2EHlOhDqrNaAo4CETEyRkEc8hXmnEfu8ceTBMtjA9sLR2AoftS2muD3LizaBUTLKKWdSLthS8MYyb1qmLGQs3gRiLd3psCmHeYFUlIiz1BMfK8SUmFJWKiZjSAhTKgzDREkqLS2A847FyRF+Nv/EF/fD2C5EpqjT213bklxLNnVyppSqcaeN31KUpUGsw7Ud3WLJbHnC4viYrpsfek0CtbzXYYzR6CZdEuiO06TibZPCQnVwUOcaGrRXN04D5IFYgo4SMLFenXP+7Akn9+7TLRbkoln1ZrNhs14xDYOyTVsl8rx+Jj74yld+QAcHawmKXIOZXCgxHWbIYtJSKSSsWDo/R7p91A4kBzmqtlSurBLW6AxUk+nmjkwDUyZhMG4vK690WsYaVSj1jc6eGO3tUWIttRaMK+AT0VqK0YFMLZ0FQlGSY+c8vnHk0ryQGv8HWa6blmoIRUrtd2iwlBVEUvWFci1NlpLVSeXIuOvZnq9ZPXnGuNkyP1pwZE7wi1a14YrKllB7KmWf4ST9rUKYiGE6ZD/mCsEqaHahAVVCRIeyVSZFodOpQsv30Oo9+EDkZlB845QJsRAigCo1WOtJaaQfRtp24vjYMOvmHB/fYTf0DENfj4MWNzbEGJAYFAiU08ERT1NPv9seSIqtVcaOlBPrzZppVHTf3fv3eOnBfWU3bz1t67XET6n0SoVhnHj06Clvv/UWq9VKhR4FchZiCLWU6nC21TEDDC+CKvKj6UHNWhovWHQGJqEzHqYyNoRc6EVLZg7VqW+WSxZ3J45RBc7WteQpM/WTXqaoNXtrFM9fDNlDnnTq1llDY4XeW6aUGENiiCqsl0qBWKfwUSZpi8GJIoqcFbCOiNBHjVD7ftRh4lwodks0lqMxsDi6GcHCXcyMKR8E3gocyhEpJYoYcs0aU64QVefw7YxuccTy+ISjk1MWiyO6boYRoWkaLbMayzgOFX2pcFJjhBCVHuUdpRljKyLKYmzQ9wwFbxPRKIS03205P3/G6eoM1zTkAquLC1YX52zXa5VKSVGZr1F48XXbdruqIAU9eXPKNXtK5FBZxJXiFaEo67oB18zonMc5z6yd049bUhxwptA6weMgRSJK72VswjWCx2CwGFtqBqWIN2Mtrmlo2hnOd4jxpByrnHuuEjSCaxzdoiNmIZZIGSoDQ4qESfORplXmgXZ2/WXoUp1TLrEix7T0WSwUSWSioktLVm7CrH1JSqbEic16zbNHj3n65kPGzZa7D+7imwa3bGmKP8zflBSVZYLK+hAj0zgwjeMh+y9QZXNyHcDOh/Le3oHFkhiGQRlWqqz5nu0gX8mo9ufXddsQCimBiOfOnfu88kqP8w0hDDTOMZvN8b6roA/VDAshImJpmjltGw7jChp0qsjhfoZbM1CpPSpFT4aUqxMT5rLg9O4JP/xHfJn79++xPFrgXQPFEMbAOE4UMtM0sdlsePbsMZRIiiOIMmEYMVjrK6x9TtPMEHHEF8DM8REHdT3WCqAnUBGVBW6wpKgDZdN2xyZnUgjMZ5521uDmS7qYqxy2VYRUFOIIE4XOOhrnMUbwKCWK2BotiTqcnbWMKTH4iI+GkBMxKZdVjJGQ9CRxrtKIIHijvFeZzBgDpExIqdKzGEIW+imRNz3DDdCeAAwFQiqkmFU9WDG5WjaJCWvQaMg1uKalJYOFo+M7HJ/e5fT0JU5O73G0PKJrO6wIzjtOTk5oaxkuVzirMdqvO1BrV+ekQzgq1d11M3wTMfsBypSIlXdxGHouLs65OHumQ5u5cH72jNXFObvthmmaICZibaOoQOA1r+dupzBz9HvnrLLqJeks0p6hx4rKilgldTnApIvrMEVwyWhpuQRMSuQxM+WJYi/ZOawHt+f3MpfINbHmINjnfEPTdDpMGiEVQ6qlPKRoxtVaiJ4yFUpImoXkRIr6etY5EMHfQE9vzxqRc6olMs2idAh2IpVQJeqzZjBSDnIiKQXGvme72rA5X2kGNZ8Rx4lceRtVVmTSof9UDjM/YQqM434QPBDrcHNKGZv2YIekXJZRb6/MS+ScGUeqEvclUzxclvsOM4HXbDGqEnPTtNy7/wBjLbP5kmHYHnrxzjYo00idnYsRMHjf0bbhMG6SUtCstuTLAUAplKLHNuWy35ZyoqDkr8vljJdffokHL7/E0ZEylaeo6hFDrwPQ4ziyWHQ4L5QSCWGsx6vDuQZvtHfVttWh1tf4pO2jFWHFkMUQ6xCkGKFtPA7BJ8+QAkMIrMcLLs7POb5zxL0Hd4kYknEMu5446CZGyIQcKUZwbUvXzvBWSCViRlTyAf0xDeCtZcyJNiW66ImlkFAuuVCHxFJKOKuZgPKkCdYUpGiGR8k03uGdcs0dLZYsjpbEFLk4v/jEF/fDWESzm0upeyiYKpedEG+xvqVbLFmcnDIrx7jWc+fuPU7v3efevZe4d/clTo6Omc86/d7OsFgs6OaVLHXvmPRHxDsFunjnEbHKQoHBNS2L5REiMJvPWVlLjpGQJmKOTGFkvT7n7NljMjp8fXF+znp1xm63JYSAZJXxUy6v61/Pcbsl1wx0X/owFRympCNSj42iJLelHOD7KUVSjJQYcaFAFGJIOrDrMuILpjOYpoJ8nOAQSiokk+sSixKo1jUwRpvOYg0p65pkk4nEylyhGYk4sI2lTLUcmWq5r5YkjXO1lHK9ts9w9htiTFoeLVmBDC635BK1h1TXOVtF4ZUcNShIyjEodSiZnClRs6QQdJYyh6gZT51TCpMyJ2iJL6iT2guWVp7ELLmCghIpamlZpBzmnGIIipasBK3e+3cg+q6OX1zbeuZJofG+4SX/MsvlEUfHp6zXKzbrNWEagKjsDkRK0UxJMAqCmikTho6bjFr+rKheNa2S5KzySCL6eCOVSsqLglwqs0wIWuaOIekeWntRMdah8vqbhzBSSlZghdG5M+87vJ9hnUfE3LzkexaD7qFaY/bG0hqrDBPOUILKhodhZDcOINDNZoRcoBimMbHb7JCU8UWJI0WU2qVpPd6o3DVZVTiTTUSnQ7bOZbqcmWWF2+pxXvQgrw3BlNIBlq1KtYWctElectEBSa9N56P5jMVizmIxZ7vdMQ3Xz7wN2nuTlDBV2ybXTY4CKaoUdKFgnKXtZhSBpuvo5kvaTqOXvRjcYUBShBAmyi4zDUNl096PnQib1Yp+1+tgnfL11JLCHphhD7NXh76eEaX+2Wx4+uQx4ziSUmaz3rBZnTNOQ5VIUMCKqZWG67ZpGBRZtndORq6U3mrvB3STrPplOQVS0M0y1WNF+R0LOWZCSMQQyaHgUF7Ipr6OgSpCWCChWfCeM9Epe7YxASnKU5hqvyZm7emYEnWAVYrOme4VeWu/MSUF9thSMP76FzTXHsfeSeUcyXkiZRXKc2kiZZ3lcgJUDreUdIu11tA2LcvFnNYY5rOZIhyLlpGmYSBMIykGzXILpKiML1PVwJqmiXEcD2W8vfpzSqmuUWW30DCEUH/LGMbD2As0B4d0QLTeAIrPmHRQd24a/UyKX7SkqCXMWNdOZN/7S/W5Du9bDbSbAe8bhuGdnV6p+4cKj+oQtbMKx5/N5zRNQ4qZi/M1FMOm2+kxl+sQcTFVZWKotFRDHT2Jh/5dKdQKgTJ3KBGyOsVP2j6Sg0rFMoZwKD3NMDRtwVgde7DoIJ4Ti2SYdhMXz1a4pqJ2ijoxokabgsEZjWal1rBNgVYsUSxDEZwIrbM4VGpaezE6c7WvxcZotQaO8gV639TPq2i0lGN1UJpVeeeZdZ7OK9pssvq5b8JECpIjLkWsQLHCZASTI3kamWJg6HumcVC2YYEYAtvNVksZfc/q/BmztqNtVMnWGIPzTiUh6sG6n50oBaa+5+LsGauzM0Kdt7JV7+Xi4kKBFTGy2azp+63SoqSoEPcx8+TRQ1bn5zofMY702w0pjgilMp0LoQilXP8GMI3DwUlbo05J0aIFQRFjOSVKiORpItXoPI17QUAthTZV46pYZcJPUbkn01gQU8gUjNFyU6l8ZnqCZpLRjbOPAyUIfozY1pOo2WiImh2njKRSgUFgckFyLUdGAKGYTCzaEDc34PGV0DYdmBf2gISUM4ZCMMqon5yWnqVG8KmmU75tWN45xmPIIbA8WTBbzlXgsN8xjj1xGg/lvpiKgq5qVSSEyDAM7PqdzmB6X/eAS0RvrvNY2lPKFYGqQeseaBQrk78x5hDA2hvIoJy3NdPLxJiYwkSqmcueA7/UHqqYfHBSl0TXDmPU4ewdnXMKDilQASSauXvnVSCynTHrZnSzObN2xrALvPGNhzx9vMK7FufdQXna+4ZCYeh3bNYb+t2OVCmM9qhSa/csIeoccymHyye+Xh/lwQlhCJlpCnijNfucMsZUlFiMOnBYwGMgJMbNQO6U1DGHhMlGlWytshhbY9RBkZBiMKVoydBYHeCtZZlirPYIsjqolBJRhGQgu8ocbZX1uGn2B3EiRHdlrkd7DYdoOkfi2EMMtDdwsIJG+AqpjRAnShgpOTP1ha3RH32727Ldbum3Oy0dOYfdrDTz9A7vdMDZO4cRUzWjlA9tL4NwQGCgcuJjv2N1cUa/VV0XqdnmmIKucYxM06jUVjnpSRRDHeZTFou0j6yjNsVBSWozCvXnBiJUsnLkqXgjWAqm5AqkqY3laSJN6pTSfvg5BC0TGUG8r5pPDc4YsnXKJ5eTspNH9HiVjCRVRDUlazpVtZtSyYxOuftcF3Cdp9hCllQzeg4E4CWiUhsxIfV/EsoiULIGJkU3/2tfztr72YNNcsqV8HYP9NCANR8Gdjn87gXBNp7ueEHjHZIyTeuwjWVMI+M0MI0DMUxQS5qHnnKI6qBiYJxUoM/vlX2LBgVZ6hxWpY+4HMZNh/7U1eHcqyAJYwyXgibXZ2Kc8pbGxDQFpnEiXWGDMFYose5d01QBTfGwvvqR945CKYqcrRIaoqU8MaZy/fkD24ZvlM2HYuh3E48fneHdBucaVdadLZgvMrNOf8NxjIy9gibSfsbMyCH71FK2IjD36/wiVvMj7cqlQIg6AY0zB8j3HvWhfEwTxEhjLGIcpsC47QkpkUKitY5F0zLzjZ54OeJM5SuraBQjCnDonKeYRKze2oqCMWKNOLx6OMSYA4ty0ygSSxVKDdFZPTirdy9o+SelSOgnxs2WgmF2Qw7KV8b1KUWG7UaHcZ0jby2rc8MUApvdjmG3YxyG2qA3ByipMUYZnGtvRcQe7hNj6jwNtZSnKVQpmZJ0dmLod5ycnEI5waL0/6HW750xzOcdIewUwZW0xBMmHeLNNSPZIyi1TKgT60WKKixf+3qqRpNm5ToAm4IeZykHdUSjqjSnKaiES1SaLFNQKQSvBLDGVGVWsXgskjUYsICMOq9HjEgdSC8isEe3iZAlEeyE6QbczGs/0ekGYBAkWy0LhkgcCowJmRImZkyUAyNF2TP43wBZbArxgIZUgtvqpGIkl4w1UUE+FYWqjSgNVIoYpPHYRcG1FpLywExF+5njqIKbKUV11nUoNQU9x3VPqftKCNgqSigiFTWoEfwl8GHPfFEOm6beweX5IHKJlL0BkETKjqnuYVOVzElpQiThG6EJwjQVpimwWmU2mx1TCJXCSQ7gEBCscXjXkJrmMKi8z6A0izLkXAhBtZxizHifCBFCKDRNS+MzFItIxNmENdrHJztyVlqz/TqZmpnpGu4DlQjoMLeRm56DygmKDhp65w6aQinusyctWzh0fkOcTtD3aSSEiENofcO8aZk1DTkKMZYqUZAPP4IR6qSzo70i3ibK/EhKEVcnwZW9WDMRayy+CoGVYlSptOQKzy4HhE9IkTGpemwKGecbZu3N6EE57yliMKCyznFiMIapFCJK17R3/KlGqpkr0uJ1E7tskcphk9jH3ALsawD7YTxBCUxzTrSNp9+uYVZoGq/oNiNIEXLN8FKdYC95f+JfYlsPJKZS6nCb9lKMvYEaP2iZrA56lqQULSkH/Q5h76R0vouUkZzrR1fxS1OkXiBXaL6zNSoVQVKhjJE8RSRGLdWhLCDZKKpHxGi2JCgd2DhhGweNw+0DjFLBEKGQh0Qe1EHZUCBpwJFrRpIyNzKoO40TuVT+t7yXVN830cEQqs6SOingst8pKrshjUOcTtCrjllkqjNOmpFHpJKoppQOpb1pmuj7qvt0cDZark6xZrB1Izbm6uZYgzHhwMFnrjinlPR3yfn6HdQ07Ut7ij6MMR96NzpHp2QD4xAYh76StAao5/m+1Kolv8pHmJqaMZbqQC6dFFSqohDIRci5ktWmUucjKzsPe+XnrE4vJ3LtNekA9TvPZQ3ytTeFaFnc2E8+yP9oJb4w4S00s4blXEUHRVCkTgFnOL26tQAAim5JREFULMbvp8yNcsVVCXZnlTfPG0vrLb4yPsh+B5VcWSGqwJsI1llcBkpWwbOQlRkgRIW3Nx7nVY9nP8zqrcVacziQQ8okuXRQyaiTjSKaITQ6UzSf3YyDss5RxBLRH32MgW3MDFnJdxMFMYqwazujVPkxsNdx2fPrXSIS9ptE1Q8q9ZvXzIm8r2nXTTxn+n7DkycPOTmJ3Dm5Q9N0eGvZ9bsD1DdGVdsUyqGEI3snVfsNYuTy4gzGX39WKrXUnIPqD8UwkcJEyZoBkvZkx6oJJofS5955F80G6/rtmRGcEW1m56L9qzFSpoikgsmKCCwovl6MUSkJCojOYJUQKS7pxWh2wR7MUe9nSJSQyFHZKpLRYK1gSCRuYlKv7/s6W1NqwKHnVUyRaUpI2aPtal9NZe60JCl6LBSDQutRcEhIKmC47wuVlDAYcoZQb1OY+aSRfy0pm4pi1PJiPjifq4CHq//vHdOet09ELjXRbshB9f2o5AI5E5MhZUvJllQsta6LoJWT7WZds8d0KV+SVRywlP1coyICU9yDGOzBabN3OtSMMSUSgbA/bysEf5+hxzQyjYr8VaBWpq2KCtSgWPeMohRMMZJLxNjKduM+eT7TjzYHZYWmsgfPulap8KOqbJqaXpZ6EJXD1LeKAhrRxqQ3hlYMrsheyolE/cGKRmkk0ZNX9puqlmBK0VkHY2st1Nbp/aqbIkYOG7Yxtub8CUMi15JUyWAlK1UQgsXSOE9zQyCJbBS6X0Q5zpR9QGgAWzJY5cxyTmfCpmlCdqoFlYtuhlYu68Iilc8tpwONTKn197LfnEuiFIX1plr+3PUDxqpyZtMoULzvN6w3a8Zx1E2qlkr22/beTV0mbbW0aO1Bl+e6bdxsSdNEDhNxmsgxUFLU46D2IaVcqtnuba+TVXLVQoiJYg17CVyLcsQpb16khAyxOqakr6mSGzVrFWXbl5rxEkAsYPdAEnOAuZPrsHsoCo7ICpyQIoextYToYPo1W9/3KplhBSeq3rrPVkKIkAZ639P5ga6Za+9P9vRIchgEF5MpCaYY6Yee3XZDv92RpkmrMsZCEXVg4ZI3LsQEUnBO+SgVLalZ/FXI+PMce88j9kpRxK+C3OS5jOv6bApBj6OajerJ4yo7h2BsBHHEmNn1OrqR6z5VxFY0XawzXkmDcqc7WcoZe3BQteMusHdUe8XhfWtYaaJ0xklQEuOUJkL0GDHkkt5ByrsPTvdBqK09/+XymNlieRBE/CTtI+0gs3bflNdafC4w5kTJVYOFPcBR4dN14pDGovh5UYCEL1qGkUwd7NOG6xAjUx1YlSI4p9lUrszaRqA47QlYqSgSI4celJh9/Vudo06Ma7NW6uahMjyaGoux5GLw1nAD1SgAAoYkhmwc4nRgGdfQWQfGYhvly7KV6mi72ZLMORK05GesMjdfQmdrAz9FyPuGfD5wHe7LeiUHDozUWftOm91OmT2sBwrjODCOW0IYagO2ntSlllz3R7qUy8azuWze2heQ8n+Qbc8vVBwvTJSoayCUKqNey3hVBE6HJUotCJUDIKSkSEmWErVcaeqMTylCCok0RUhJM6faOzHVwRnUo4iynVLLArW3qppOSvZdSVbRnqtosqXHa9KNVt9eXzchN9Iz2Q19ZQ93iDUHMJLqVEWGPLCzOzrfs+wC2Yg6o1pSlqolhbXIpEjRzWbL5mLNbrOuMz4qhipGj5eYM1OqFYSUlCuyaVRyJivYBXSNLbYee0KM+TCQvieM3due5ohSMAdwwfUHpSEp2jhXpB4o+W0uDhGPmTKYhpRhGAdinJTHUTfVes4qcbAS+QrWtCBZj0cxiNjaj750wiLUmbyqRFH3x7J3UrXNoqwbmn2VHLFWNan2WdTVtfXOMZvPOblzymJ5jHsBhNtyE9PUt3Zrt3Zrt3ZrH2Q3k+fe2q3d2q3d2q19gN06qFu7tVu7tVv7VNqtg7q1W7u1W7u1T6XdOqhbu7Vbu7Vb+1TarYO6tVu7tVu7tU+l3ZiDEpFfLCJ/9pt4/neKyF/6JD/TrX04E5EiIt9x05/jpk1E/oCI/Kab/hzfSiYiXxWRn3nTn+PWPrzd5Pn+gQ6qHlC9iGxE5KGI/H4RWX6zb1xK+Q9KKT/rm32db1f7Ztb9dhN4b3tRx/MPJbtdw0/ebs/3d7cPm0H9nFLKEvh7gJ8I/LoX95FARG6GufXTZy9k3W/X93qP549q3yK/z42u4bfIGn1Uuz3fn7OPVOIrpbwB/BngR3+Yx4vIrxWR7xeRtYh8t4j8E1fue0eJrqaR/5KIfC/wvVdu+xUi8gMi8kRE/g8i706ZKyL/RxH5hoisROSviMhPvXLfbxCRPyYif7B+lr8hIj/hyv2visifFJHHIvIVEfkVH2VdXrR9lHUXkT8EfB74j2o09q+IyBfrWv5PROTrwH8mIj9NRF5/7rmHSExErIj8q1d+v78iIp97l/f7++u6//RP5Mteo32M4/nvFpH/pq7HHwW65+7/x0TkvxWRcxH5L0Xkx1657z2PsXp8/gkR+S4RWQHf+Yl8wWuwj7qG1X68iPx3InIhIn9URA7rKCL/vIh8n4g8E5E/JSKvXrnvHXuEqP27IvKovtZ/JyI/uj62FZHfIiJfrxnJ7xSRmyHc/Ih2e75f2kdyUPUD/2zg/1ev/1oR+Y/f5ynfD/xU4AT4jcB3ichn3ufxPw/4ycCPvHLbPwH8BDSq+LnAP/cez/2vgR8P3AX+Q+CPXz3wgX8c+CPAHeBPAb+tfgcD/EfAXwVeA/4h4FeKyD/yPp/zWu2jrHsp5Z8Bvk6Nxkopv/nK3f8g8HcBH+a7/S+Bf7q+7zG67rvnPtc/Avxh4BeUUv7zj/SlPgX2UdZVRBrg/wr8IfQY++PAL7hy/98D/D7gfwrcA34X8KfqRvlhjrGfC/wJ9Pj8Dz6xL/mC7WPsCQD/FPCPAl8CfizVIYvIzwD+7Xr/Z4CvoefsVft5XO4RPwv4B4C/A123Xwg8rY/739fbfzzwHei6/2sf4yteu92e71dsT7D4Xhfgq8AGOEcPmN8BzD7oee/xWv8t8HPr398J/KUr9xXgZzz3+AL8o1eu/3Lgz7/b89/lvc6AH1f//g3An7ty348E+vr3Twa+/txz/9fA7/843/GTunwz616f+zOvXP9iXcsfduW2nwa8/l7PA/72/rd6l9cvdY2+BvyYm1yn61pXdCN8k0oPVm/7L4HfVP/+94B/47nn/G10k3jfY6wen3/hptfmGo/N//GV678Z+J31798L/OYr9y2BAHzxynH3M67c/zOA7wH+e4C5crsAW+DLV277KcBXbnrtXuCaflue7x+2NvnzSil/7kM+9mAi8ktQz/zFetMSuP8+T/nGB9z2NeDVd3kMIvK/An5Zvb+gUcDV93r7yt87oBOtzX4BeFVEzq/cb4G/+D6f87rsY637+9i7re972efQDPi97FcCf7CU8te+qU90M/Zx1vVV4I1Sz9hqX7vy9xeAf1ZE/uUrtzX1eYkPPsY+ym/zabBv5th8/lzcn9OvAv/N/o5SykZEnqLZz1frzd+4cv9/JiK/DfjtwOdF5P8C/Cq09DoH/opcqjoLuuafZrs935+zFwYzF5EvAL8b+J8B90opd4C/Du+rW/1uzLVX66CfR6PY59/rpwK/Bi0NnNb3uviA99rbN9DI6s6Vy1Ep5Wd/iOd+Wu29GICv3r5FT2JAa9DAS1fu/wbw5fd5j38S+Hki8is/5mf8VrO3gNfkyo6HHo97+wbwbz53HM1LKX+YD3eM3bI267n9hf0VEVmg5dI3rjzmHetUSvk/lVL+XuBHoSW9Xw08AXrgR11Z75OiAIRvR/u2Pd9f5BzUAl2gxwAi8kv5aI3Uvf1qETmtddn/OfBH3+UxR0Cs7+VE5F9DM6gPY/8fYCUiv0ZEZrVZ+KNF5Cd+jM/6abGHwA/7gMd8D5pF/g9FxKOIoat8+b8H+DdE5IfXZvSPFZF7V+5/E+2l/AoR+eWf5If/lNr/Gz3GfoWIOBH5+cBPunL/7wb+RRH5yXW9FnVtj/j2PMZehP2HwC8VkR8vIi3wbwH/VSnlq+/2YBH5iXW9PboBD0Aqqvv+u4F/V0Qe1Me+9mnqK3/C9m17vn9TDqqiPv7Mu91XSvlu4LeiJ/ZD4McA/6+P8Tb/N+CvoP2rP43WqZ+3/weKevketOwy8CHT21JKAn4O2kz9Chp9/R4U2PGptPdb92r/NvDrRNFkv+rdHlBKuUB7er8HjVC3wFWUz78D/DHgzwIrdN1nz73G19GD9teIyC/7mF/nU2MfcDxPwM9He59naEP+/3zl/v8v8M+j4Jsz4PvqY78lj7GPax/i2HxPK6X8eeDXA38SzVi/DPyi93nKMeqIztDz/inwW+p9vwb9Df6yKDLyzwE/4uN8rpu2H8rn+6daD0pECvDDSynfd9Of5dZu7dZu7dau1265+G7t1m7t1m7tU2m3DurWbu3Wbu3WPpX2qS7x3dqt3dqt3doPXbvNoG7t1m7t1m7tU2k3Kbfxr4rI7/kmnv8bROS7PsnPdGvvb3LJ8fUtSz75IkxE/p/fDijGF21yK9PyLWc3fc5/GLmNzZVLlktK+I2I/OKP+8allH+rlHJ7Ur+LfTNrfrsJfDh7Ucf1t7vdrtuLsdtz/t3tA73i1elrEfkq8Ms+YTqOH2Qi4kop8UW+x6fZXuSa/1Bf273dxHH97WCfhnX7djyGb8/5d7cXWuKTD5bA+K7697vRw+9v+xdE5E0ReUuUb++93uuPi8jborT7f0FEftSV+/6AiPx2EfnTolTy/5WIfPnK/X+niPynohT/f1tE/qkXtCQv1ETkL9Q//2qNvH6hVJp9URaDt4HfL++iRnw1ChNlO/itIvK1up5/Sd5FqkBEfoEoZf/HYQj5ljUR+YdF5G/VtfltXKHUEhEjIr+urt0jUYmXkyv3/5J631MR+fXybSw29x72M0Xke0XkrJ6TAu+/bu+xP3Si8iRPRQdU/2sRebk+/kREfm/dM94Qkd8kSu3zbWff7uf8N8sk8TtE5He8z0M+SALjeXs3evifDvxwlFr/177Pyfxn6uMeoISTz0sW/NOo5McpOmH+b9bvsAD+0/r5HtTH/Y6rDu7TZO+35qWUf6D++eOKUu/vaaFeQX+DLwD/wod4m98C/L3A31ef968A+bnP8UtRSYOfWUr56x/5i3yK7f3WWETuo0wHvw4lI/5+4L9/5SHfWS8/HaWfWXIp7fIjUZbqX4zKSZygRKjfFvYh9gOAfwwV4/txKHfm/lz/Tt5j3a7Y1f3hn0XX73MoX9+/iPLvAfz7KC3VdwB/N7p3fMu2E35In/Mfhfqc52jdP+qFHyyB8V3lvenh97f9nVdu+83A733++e/yPnfqc0/q9T8A/J4r9/9s4G/Vv38h8Befe/7vAv63H/d7fpKXj7rm9Xt/x5XrPw2YgO7Kbd/Jc1Il++ehQUu//52ee8z+N/lVwHcDn73p9bnuNQZ+CfCXr1wXlDLml9Xrfx745Vfu/xGoZIRD9Yj+8JX75vW3+djn1LfKul05xv7+K9f/GPBrP8S6vdv+8M+hcic/9rn3eBkYuSJVgQad//lNr9cLXtdvy3P+hSIz5IMlMJ63DyO38WPe5X0smhH9kyhD797z30dZzeEHU/zva75fAH6yvFMKwaHCdN8u9riUMnzIx95H5Qrej3r/VwP/einl9fd5zLervco7JR+KiHzjufuvynB8DT2eXn6X5+5E5SR+KNl7nYfvt257u7rOfwjNnv6IiNwBvgv436DnswfekkviecO3npzJN2vfFuf8C3NQcimB8Q8Bf6OUkkXkjI8nt/G36t/vKrcB/I9QRdKfiUYfJ2i29mHlNv6LUso//CEe+61qz6/r89T7r1y57wlKtvtlVAH23exnAf93EXm7lPInP8kP+i1gb3FFAqb2UK5KwrxDMgI9ZiNKmPwWVwhLa43/KmP0D2V7v3X7bL3tcByXUgJasv+NIvJF4D9BRff+EzSDul++RYEBn5B9W5zzLxIk8c1IYFy1Xy8i89oT+qW8t9zGiLIZz1Ga/g9r/zHwd4jIPyMivl5+ooj8XR/js34a7MNQ7/9V4EeJyhp0aLkUgKJSBb8P+HdE5FVRaYifIip/sLe/gUp2/3YR+cc/2Y//qbc/ja7dzxedDfkVaL1/b38Y+F+IyJdEZIkei3+0bpZ/Avg5IvL3iUrI/0Y+XBD1Q8Heb91+kInITxeRH1OrJyu0HJhKKW+hjNy/VUSOK/jiyyLyD17XF7kB+7Y9579ZkMTvFJHf+R53f2wJjOfsv0BBDX8e+C2llD/7Lo/5g/U93kDrpH/5w754KWWNRge/CI3i3kYbge37Pe+m7APWHPTA+/crsuld0YillO8B/nVUguB7gb/03EN+FfDXUJDLM3Q9zHOv8VfRhvfvFpH/wcf4Kp9ae781LqU8QUvJ/zs0IPrhvFNG5veh5ae/gEprDMC/XJ/7N+rffwTNptbAIzS4+pa3D3Fsvp+957q9h72COvwV8DfRfWI/uP9LUDXj70YrKX8CBaV8S9oP5XP+U8vFV9P2rwD+h3iqfmvfplYzhXNUUuYrN/xxbu3WPnV2y8V3a7d2jSYiP6eWrBcotPevoX3TW7u1W3vObh3Urd3a9drPRUvJb6LlwV9UPq1ljFu7tRu2T22J79Zu7dZu7dZ+aNttBnVrt3Zrt3Zrn0r7SHNQv+Q3/K6yn327mniJCFeG4kAEAYwIVsAIiFw+QSqytpQCAlIh+4eXkCuPEoCCAJaCwWAxer8ISSBRyPX1pIChYMlYyVhJNE5oG0vXtcy6OcY6YoJtP/LsYsVujIRU+Pd+3b907ZDf9easPH74iO1my2K5ZHm0ZHF0hLWGGCbOnz7lrW+8yebigpwzDz7zCp/70pdouw6xBhHR7w1IgbIPOYT6O8jhKgUK5cqVK78D+jsWRH8I0YdQX/vyRUByYfX0jB/4W9/LG1/7Os+ePePo9IQf91N+Eq994XN432CMgVJwtrvWNf35P/U7Snd0h/mde9x76RWOTo6Zdw1CYuq3DP2G3WbDMEVCLiRxZPGkkJmGyOMnz3j08DFnZxestzsKhXJlXYxYjBjECMYYnHNYa3HWUSiknMglA5mSCzkVSk5YKSwWDcdHLV1naAxM40SYIlYEYw3OWjBCTokQAkM/EsZInDLdbMbR0RF/8a997VrX89XP3i1HR0u6rqGUTNe1nN45xRnHNE5s1jtWqxXjOJBzJtXPHmMixkTOhVIgpaTHqRjatuX09JT5fM44jUzTxBQiKWdizqQYSTFQcsaI0LWexaxl1nV0TYMRg6nnvxhDyUJOhWmaGMdAmAIxRRKZTKZIIeVMTvr5Yky6fxVhPU7Xup5/8bt+SrGuheLYbRNxTEgqUHc4KUDJ5CiEaJhCZgwR2hOkvUv298j+FNvcoZ3f4+jeZ1ne+8xh6MlaSykQU6AfR1brnu04EROUDLmI7gFFCClfXmKCPEEeyXHCUvjiqy/zxdde5vOvvcKdk2OMGHLZf9ZLu1qFOzpuPtH1/GQGdUuhsHcwot6rbpwFyPLOVK3U5SwUyOXSMZUrr0F1fPvNk1LfJytNhOgrZqBIrtQYovtuMWAKQsEZ6LzhaN6wmHfM53PAsulHhj4jWS/vZJ26PsspM/Uj/WaDE0PbeMpiTiKx2aw5e/qUZw/fZnNxgYhhPpsxbLeIgGsaxFkQIU+BEpNunM6o87LqyAEOfumKlZLJOV06ImMR6/Z+i31gkEs5RCRSfyRxhrb1dF1D6x0WSGEiTBPe+YN/u27zTcNiPuPkaMHJsmXZeZwtkCNiEmJAGouTwpQKqRgCMIRMjiNpGigp4J1huZghRsDoSY8B5wRr5B0Oyhhd55wzMUIIhSkARvDGkGMBEoYMOZGmxCiZsR/JIeNmDU4KKUzkkjFGcKYwaxweIUhi1lrms+uX5AkhMAwDpeT6nS3jNIEDEct8PqdtW4wRvPeM48R2u+X8/JzVal2dVQb21DmFlBLTNGGtJSUF6FqjwZa1lmSEqWQS+zhLQAypFKaYcKZgjcVZi7EOjGAsFDGI8TifQMB5C1ZIOTKFie1mS9+PVC9Qf7frNWMM1lrEWOZA8JDHSJwKKRZy1vNSt9BM0xZ8C1ssu9KyHRvC5GmT0JbA03hOfloD9P06UsglMYXIdhgZp0jKQq77Y86FnAsxZWJW5x1TJucIOWAlMXfC/TsTU0zkUigUcsn1N5RD8PuiW0Sf4BH/gz3r1Vvf7WuU5/4HPXau+CgKos6tLhIU8sHd7Rct1atySCEM4IzQesus9SznLctFS9c1mj3tEiVFSk7onNrNzEuWAjFMjP1A4zxT1xLGkZQC508e8/Ttt3n28G36zRbfNGyPj1mdnxNTopm1NLMZ1jvGvmfa7nDe0XQtvmux1lAolxnrfpOo0URKkWkcySlRcqHpOhpj9cC7siI5Z3JM6swoeO+AgljBGjA5ksaB3eqCfnOHtmmRmkFdt7Vdx2I2YzlrmDuhKROERI4TZuqxacSTQDLGQCwFQ2EqgRwncpooJdJ4wbctxjqMtRhrsdZgXcEYDar2G6eI/pApG1IUhj5TUkIQrBhi0QhfUiaHSMwFIZFCpKSMlQYrolF/imAtxhi8tbjW4K2h7Txtc/2E3MaY6lACxmhVZLfdkZuMsx7vPLPZjK5raZqGYRhxzjFNgd2uJ8ZUqye6C2i1BWIMjONlIOqcPVRiQrKUrBGj/i8UEWIq5BzItq6FsVjnAANFcE2tr1iLbzyzGmAMQ89qswIMqcaiOecbOeMz+nGN081XBELRIFBjxULRMhDGFIwFsbAeLReT43xyjMUwz9ClidWzM1bThlR0jawIRgqFREqJMUZCLJQslKKVgH2mm3MhHS6ZlDOlZGaucGdmOV/vWG/00rUN3nuMEUQMss/EXrB90w7qHaW9H3SnXq5+kQ/+SnLlotf1EK1llvr/PhorJVDKVDMEgxGPFXAGWu9YzD3Hi5bFvMV7TwiBXT+xXm/Z7nY13X9353odJgZEdDOfxoHdeoUR6LdrvvGVr/D4jTdYPzmj5IRZLthcnPHW66/TzWd08xl3Htxnvlhw9vgJF4+fMp/POT69w8ndU9q2JUs5ZKY5F1KM1T8J4zBwcXHBNIykmLhzepe79zsMRiP5mg1PY2C32zKOI7lklsdLpnFk12/ZrC/YXJxhRHj09RmNd3TdnK5uNv6ag/7l8oh51+JLYVqdEcnkFCgxkOJECoGcI7kYxDis9YhpsClAnhACzmq2YJ3FOIf3DYvlktmsI5eJXCJCIabEOEzkkjDG0opBxOOMUFKi5HpUpUKIiTglxhJpWoN3BmcdxoKzHmMMxiSIhXFIGJPomobGOWzTYqwlxXS9iwmcnJwcMu0QAinumMZA2zTMmhmzmV5yTux2O8ZxZLfbkVLEOc0w96aOyOG9J+fMNE16jHhP1zT4pkFEiDEgZIxAmCIihoLUkmGGRvBNo0GDbxAEEcvR0QnHxyecnt7l+OSE45NjUgq8/eghb771JohDrKfZ7ZjCRAzh2tdz7CfECEUSKWZSymRTsF6DvZIMKek5a4wGijFntpPh8dpyNhQCkWUa6MbAo4vCwwsoYilGcBSMJEAzn5QLMRdydVDG6N6ZUtZMqlw6qJg1DzuaNThaHj85Y+mBnBiGgfv377BczHHW137C5fd6Xz/wTdjH2D72Uc+VW97nw71blvTOl3uunrl/h3LZqzJArs5JHZXeQs6kNJLjDikFKxbnZzTeMG8bdU7LjuW8pW0dlEI/Tqw3PZt+oB+D/njv8jmuzWp5LYZIv92xPr9g/uwZw3bNk9dfZ/X4McNGS3qlJI0wp0DTdXTLeY1qYbdacfH0KTkEmqZheXSkGUwp7PPDGCL9bktKCWsM4ziyXa0Z+4EYEl07o6SMmHwo25IzYZzotzv6XU9KCYMwDT0XZ89YnT1j3KwwpXDx5pvMuo6j01Nyyfimo7tmPg5jLDkmwm7HFHpMihjJkOMVB5UoYjGuofiWYhOEkZJHjCS8B4tgvcF5S9t6jo9mzOdzhlFIyYKADYEUEiFmyIUi6ti8M8xnDSVpH0qSgWSu9GUNzlj2v0xKWgnI2VCKIcaoJbUSIQutN6SsDu66TcSQs/ZtQog12x4ZnGNsJsZxZBxHrDWH/s40TUzTtK/0c7Vkb63BGFM3Sf0+3nu6tmE2myEiDKMwjgNTDXI06tfPU0Qo1WEVBDGWpmmZzeY8eOllXnrwMg9eepnTu3dZHh8xTQPOt2Aszrccn9xhu91wfnbGk6dPrn09Uy7knEgZpimR4r50bhBJWjozWvUwVoN7SdCnzLNN4OluJFLLlo3h8dPAm08DYp32MclISVBi7cBJ7TsZEIMxmoXnnGtPSst9qRR1UAJOoG8sz85WuDwxDgObzZrd+Aov3b/L8dExs66ru/E+aXgxIf4LzaA0MdEm6fsnhO9AR2De8cDL+mrR1iiHDCsnwtQThgskR7zztBa6puNo0XJytORo2dI1DqEwjCPrzcjFdmA7RsZYSPuM7KYo0Wrzdtj1PHr4kLHf0VqBNBI2K2K/IQ09IWX6YWDY9uzOLnBty+xowWIxo5u1xLEnTwNpaslhqo3mTBY9CPXEHzl/9oxpGDSKjZlpNzCNI3EKxGkih0gWoyUQEXLKhGliGkamQR+3M4bt+oKnb73NxeOHyNBjgN2TR5x1Dct7d0klszy+w/Hx3WtdzmGcYJoYyTDu8BQWXYORQp56YpgIYQIE6zuKn8huJE8JkwPOFhovZDFYJ3hvaBuL9wZjtGcYQ9Y+QvFYm4lTYZpGYkqIgHeGo2Wnj50iUjSksgaaxuKdwRoIIRJjoh+mukEJORtyFkLMTNOIM4HWNxSBmK6/UbrZbA4OKudETlkBCUNgMCPb7RbvHcYIOdfe8hW5BC0lXZbR95WP/b6Rq+dp25bFfFZvS5RahtKytP7tvcM5j3VOM6qaAcznC1568DKvvfZZXn7lM9w9vctyeUTbzemHnuPTLa8A9+6/RL/bsV6v+P7v/z7OV+fXvp7GaUM+l0yYEjHkQ8BdUsYU0SDGap+zIBhTCCmw6necrwsxT9jSkmeO9Xrg/HzEeY9zTh1UjuQUySWREYqxYD1ide1EFDBRco1h0XWM9ZxPOZFy4mKzJYwbnj57ysPHjzhbXfCFz7/GF77wOe47S+OcOryq3PEiCn4fyUE974z21/f/v2vD7OCkyuHqc6/6g67V6vM73NYBjSYGrTlnfbGcICn6BCYcc2ZeOJo3HC1nzLoWIzBNE7s+st5NrPvAEAqhSAVZ3JBzAh4/PWO9WrHbrDl//Jjt6oLOCp0teBJNVjRSzJFpiOQpknYjtm1I08jZw0d479idrxguVpAyVvR7bba7S3QjsNuuefZInWDTeAQhpUwIiTgFnAi2gHOOnAtiDJnCbrNhfXFBvxtI48TuvKHfrNg+fUzabZgLeBFk6hnPn/H09a8TY2S8N/DyK5+/1vW8uFixyxlfEjZOLLyhNYJ3QkmZHAJxHPXETEUdshnIBUzJNCYRnZBEECt472gaDyUTQqhtNUPJpmY5Rmvy4jAiiGiPbjZrSTEzFnQT8mCt0DQWa2uhOmn/dJoiORes1d5eyoVUIIXERGYKetbEG+jpHUrDKRNjPvQrKYVYCjEGQrCH0pGuhcGYy3LSHskH5dD/0EheapKv38taBRA0o8Og6MecaxqWEsY5nBiMdfi2ZXF0xOmdO9x/6WVeevAK9+6/xMnJKa5p6ceJs4s1m+2Gi4sV1npefvAy1lm22xUpJy37XbMZWxFjgmZKUqAkctFMOlUHlbBEAGMoYoiZ6sCylvEolJy1J5cCxaJZfCnVuWfNjvagNVOQUihJQVEll9qTurSc9fPknEk5McZEiZE+R4ZxIKTIMAVCzvTjyOnJMYv5Au+8IltfwDb6sTOo553T/u93c1Lv71v390ktK8nVqweAhaCp/d5dWXQzsAasE3LMuJJpbWbRGRZzz2LeYq0jTom+D6y36py2Y2LKQsIdMrubclHf/wNfpX/2lO3FBbuzZ0ybFU3nMa2naww4i3gLWRvvKSZCnHRDFeHZWw8J40jc9oybnu3Fms35BY8fPsZ1c3IFRgiFod+wOn9GGAe8tXhncdaTkqKjNs+e8fStt3HGaInVWcQapmmi73dM254wTjgrpKknrM5oicy8p7FGm8/9lrNvfI1hu2OzWvMjf9xPutb1fPL0DJsSDYW5FZi3zGe5bpZycEwpJa0Sy0QqhWINzhpaIDsYs2ZRzjqsdVreGpOCF5yr2YFG99ZYus4iohtQ0xi6xh3g08ZaxBVFVVpDFoWhUwEDIURCTDiXNBOhQtuNkBKEWtoTc/1H6WIxY7cbNBtPgZRyHT3Yj5ZUuLdohqkOSkt5IoIxGYiHfWEP8ba21GBT1zBMIzG0tH5B4ysyssLTtYIiiNXeXNtZ5oslr3zmM7z22mc5ObnDyckpy5MTrPds+4GnT5/xta9+lfPzc7x3fOYzn+E7vvxl7t+/xzDu2O22vP7G1699PYtkQJ24bwxGqOjPTImWlCEWyBly0MzHeE+hpTOek67DuBnzmcfYQusdXZtwTs9nQSCDzY5UaknUGMRaimhvWUux+eCg9uCUUrImEzkq8EwEMR5EGKfEm28/Y70duNhseHJ2xg/7/Od45eWXuXNyTOM98gLip088g3r+MRo4fQDoeN90uor3O/ynuZMiJOWwCIrlK3hnMdKQxwmTI95kvEl4i26kudAHzZxWu5HtmBijkEVA9rCLfRX1+u0rX3sdO2zJmxVmGmhToM1Ckw2u1LKPtWSbiEbLADFXxM8E58/O2O12lBDJY8R6h9lscO0a4xuS7shAJoWBod+R44Q3greWxnliLowxs9tsOD87x1Skn208rmu1mhojm7MLhs0GQ8aWiEkjjRSMKKrNFkjTyPj0GTFotHXdttlssCnjKRTvaIwwTgErtkac+z5AhhK0vJET4h0eT8IQEULOxJQINiImkGok771m5Fq6UrCEsYI1pqL9BKvJgUbEMZP3MxgCqRSkzkmJoYIGLFB0PMAIUgRS1gZ5UQiwFbDu+mHRgkKX91D6ki+7yofQUvbzYAbv/WEOTst6PSGkd8Cn9zNRCgxRNOk4Doxjy6xtdYSiIhn1dwLEYDOIdSyWR7z04AGvvvZZPvu5zzGbzWnbGcujYxrfsOsHVqsVb7/9kGdnZxwtl9y5c4qIYbFccnyy5DOvvsZrr3323b7yC7VxFxGxuMYQS0coEDKEbJiKJxZLKpaULRmPpcHbBrENy86zmC/xzRzjDWNKLJeO0xix1mKMzuixh5JTNKg3Ohtais7pTUGz9ikkYoxQtKRaUkSMbhc5ObJ1ZLFY40gJhmFijGvGGOnHQEmQkqID2qYh58y9k/kHrsFHsY/moOCbABO81/PKpXO4+pBy9YpGv/pnQYhAxEqhbTxNWZAkkcOgvYY8kksk5kQ/RlbbnrN1z8U2MEZIaKPQSsFJPjipm7Cvvfk2J2ViEQbmtuA6R+cNjZGD33bG4q2j84VEZigJMKQSibsttu8VjlvARo9LGZ8E1+ioYilJ54BK0hPfdbi6CoakQIqUCXlkCokStYzjZi0dmbZrMd4yTgOr83NMjrQWFq3FeEuMWo7RkyMjYSIUYczXv6opRnLQE25Iid4ZhmHAiseQSaWWPsq+n1Pn5YpCbFMRXAZCJsRMTDBMEeM8zjuglmBSOvTprNlnExWUkgohF6YpMIwTqTobhThnKBEh0lqv4B06YsqHwet9RltGSCVSyDoy0Vy/g9IBXKozQc//CuzJpYDlMA+m4Jwlx0fHIIUwReCcaYoUJh2ozXVUJO9nahSdOIwju76n9Z5SCtYanNs39AvUEuhsNuf+/Zf43Oe/wGc/+1leevAAayxiHEdHx3jnubhYHXpbIORSmEJgU5Go9+6dcu/ePT7zmVfe9Tu/SFufB7I0+OLYDnN2oWGIjik1xNQRUkMsjoJDxNPalrl4TBM5WSa62ZzZfEYfEud94s5pAzM9LpHLrHbfWyoCpiR8CZD1mB1Cpp8Su11Pv1szDANh6klhJAuUNpGjI1rBGou1DiMWhyHmxPlqIKYnGKDkROss3nt2w8R3fOGTVTX5SA7qvUph7zusVeAdmdEPMs1k9M+KaClyeSK8431q5EnGmUxjCo1zNDSk3JLQTGGaBqZpVFDENnC+GVj1I7spErO+tiHTWMO89Vi5Hkz/u9njp08RLzQmsRBoncE6C86QnANRSKpF8EBjMqNzmMZjfaPMBsYgmDoX4vFtSzdf0LZtXdNcHYgOOFqj/RYpCSmFKWbaChjJGHJQhKRtG5p5R9M1WAqueQIUnR8rBRpDEiEUwRSDF6OvmxJ5HJns9trXs3GekgqmaGYUQ2CcRpwtOKszIKXi7qUeZhQ95kouuFJwWTC5UKI67ZIKrqnOLSlAwoj2WPaN/z3yrpSMcwbv3QEQsEdKGSOYChwoOSKpkGtpzBur/Ya6sZic1SlYQXKdm/HXvpzEKZISpBq07G1fds9ArojjIuC8Y76cIQhTE9jueoy1CjcvEFOqGZQ6KAMgWumYYmA3qkp5rgg9MQ4pgrMN89mS05P7PHjpFV558Bnu3X3A8fJEy4rGcnx0hLOOtmm0VFoyMU4Mg7BarXj8+Al3755yevcOs/mc09PrFzOOY2K1grizPNt1rKcFfWqI2ZPwpOxIxWJtq1mhtBTXMOYdSUas9/jG0ScFMngvHLnmQIoghx1a97RCwZSMTyC5kIth1jiWc8fQWYYus16NrM4n1tMFYZrILRBnFOvItqHsARtWe9MhFswQFQ292dJvt6xz4e2n58BP+ETX6xqmVC67SO9t5Xkgn+6rIjWT2jurjCEpfZEpNdMo2GLAOkrWBRyGgV3fk82W89XI+WpkO0amVMAYHDrEO28cp0dznLXkm/FPXKxWHM88sTFIUWBCMkL0HpnNNCsJAULAGkOHgDHYpsN3M3zT4lyLWIc4h/HqoJZHxywWC+0FGKFk7WMY0QxAyFrySunQlM91kI9UtIlqBawO/uU4snr6jP78jNIXHAmsMIlhEgfG0VpDIwFntAQr6fph0UeLBcVZyjRBnCg5KHLPA6KjBkW0n2OsqSPfBckaqdtcsFnLlabU/kAK5IA6KJdofMOs67DWkrMOsY7jSAgTMQZms46mOVJUlfPEoHNmOC1plZyZpkiII6bAbNbivCcn3bR1IFX7Afvfy4gObl635ZgJY1Aqotp/2kfroFdTzkwhghFiThQpOqOE076bEazz6thjZJomYoqUnJHaHzGuAnLGkVIKIWUQi7UN3nrads7pnft85uXXeOXBq9w7fcByfkLn51jnaLznaLHEGEPXtjhjyCnQ9z1TGPHPHG+99SZ3797h1Vc/gxHLYvlxBL6/OTNSeLqGJ73h7ZXjYvAEaWrYPVHKREGYdZnTEy2vi82spsxmTNAkso+sh4H1sKXYTOP2TDp7VN6lm1L6t4gpI1AwYunaGUvXwdKRg+OsCbjpjPF8w7A+I889Ek4RP4fat81AAooxGOfxTUPTtLgK7FlvNnzt9bc/8fW6pjHK93dOVyHe8o6/9//vgeYJb0UjgM6xaARSr6gsAYMQU6KMI+v1hiEaNrvEMCZSqvV0ybTOsuhajhczTk+W5Ay7cXoB3/uD7ezxU46PO07mDceuYBplHMR5TDfD+Fazkjr5noxjbmv25BusaxQ6aixYixiDdY7Z0RGzxQLvdWPYZwv7hqhAHcAsFWl1JWHNGhAUgSRKATTt1sxmM+ZdS44DJicsRR27tRTjKMZA/ZwmRyRcv1DsS/dOsVnLjMN2gyHSzjyu0R4JGUrxSFFAgp596ZDllGIqLFwzJGsM2dgDtZF3Du/dlb4rFQxgsNZVgIA7QK73YAHYQ69/cLdTYdRRkVql1ECBiurT38JK0R3imk0qKqzU33U/NHv47GhJ0jo0uEqZ7W6HEf17GEdyKVjv6NqOnBPOe/p+Rwih9u0MGEMuECtyL6REEUPXdSyWd7h79yU++9pn+exrn+f+3Qcs5kvm3Zz5bEHTNLStBg0xRoa+Z7vZMI2TVluso+talssFi8Vc+2TO0zY3IJptLes1PL6YuNie08cdYpzORcVIiFDEYbmHu3uEsYVQMpFCFkhSiJJJkhHJWBtwfroC7d/zRl72CkUbr+QIIRbIkVJ6Wi/YxlJCx7RdsnrWsJUEKZCnidxo1pwzHNDkokPRIg5jXc3oGhDD9AL20Osn93rOalGvXnvekSmcUpc8YUjMvedk3nK8bDmaOcZtYj1mUtGJphCT8kddrLBDZoiWEBUWbMXQSGbROO4fzzk5PuLo+IjdMNLfQEMf4OnDRxyHJadxxt1FC7ZThKLz2HaOXSywzuKdw7kGXEtyLQqZsu9oVOtoWKxoMlsjU1edlKslI8MPBrLo5nxAZyEIhiKFWDJjv4Ew0jaeznuSE2TKSBFSKRgxFFNh1ggm1ZLiDcCiH9y/z7xx2JS4OHtKmHZ4JzilZVO6JoGSama/d05JobmpokVFDNYKzjhKXUfnPV3X6QxZJR4F7cH4Ooey79XkWu8Xo4Sme0ezL4cbEaxz2Oq4QojElEm56O9mHN467a9kAzlR4vXPQcke1FGyIvUOZ2QtItUMqgDOeUKMXFysAMhJ+xw5FbrOM5vPoEDTBmJKxJT1OK2Q81yACmkPUVGO3XzGgwcv8YUvfJnXXv0sr7z8Ge7eucOsndG1LfPZnNmsO1AthWlitTrn7Owp4zhgrTCfd9w9vcNnXnmZBw8eMJ/PmMaRxjfXvp7FeNYjnG17QjzDlUSDKC9en0ijJckcWQiL9jO0TVHmEpOxvhYBbAFXcI3gfKZp4oEn77BjSv2VpJb2ojAhhCCUaSTmHlt7oItly507JzxbLjg/05GKKUyYELEpaUViTyzNnihBNMN1jnbW0bQNL4La8MYd1B5Dd7ADXkKX2piCBRpjaL3lztGMuydLFjNHZ2E9CruSdHo6J3KMjCmTyg4ThURLkQZnLI13HHUNd5Yz7p0s6WYN5KSszNvdTXx5+n7gbF14ZAvH85Z2Nufo9D7t6V3s8SnSdQovFiFVR5AreekeFaXoyawzDHVYNBWlLwkxEqOWZ/Yb6j7aEqPUM9bY2pxPpBDqEJ+QjSKBSBOp3zIOIyFGcsxILjijfSxbJnVu2QCRUgcMKdcf8i9mM44Wc3wt4Y3DRufkSlDnXVKdQbmE1haBbDSTt97T+TmpeFISTKr1DSOYuommpBmFMYamaQ58dUrb4wAl3ux3PTHmAyQ750yJpcJ5ea7HejkrqNlXVi42UWYKUjkQJ1+nXWW6N8YeWBwOcyAoglMds6LISgZjDU3b8NnP3mO5PObevfvcvXt6WKuHDx/y6NFjzs/P6fsdKQVKKXU9Ld6D9y13Tu7x2muf5Ytf+AIPHrzCvdO7LBcLlvM5XdfRtg1d1zGbd5SSGceBi/NzLi7OGMeeVEuKfd+zXq9Zr1b0fa9tgN31n/ObDUxB+8VdJzQu0zUwTRpkJ/Qcn8865os51nt2/UApBWdNZZQpIBU1akydpSq1G1JL+FfnkoxWoSTq72VswfnC8XHDS6dHxCFgU+DtWYtQiDkq6XOOh73iALzI+7IuxAQhQkg6u/ci2vgvzEEdaqAf+tGXzzAodNmgc06NgXljOZ433D1Z8NLpMa0H0shgFIkmWfnkU4pMUyKWHpME40WjXyfMu4bT4zn3juecHM0QMQqe2O1uzEFNIbAaCk9Hz32x3JkfcXLvAc3pPdziiOJ8JXLUiDwXlEKn7JFQuQ7YaZ9CN04h5qyInWFkGkfW67Vy6dXbc84VFdXRthp9juNAv90dYMF7YsvOCz4rq3WIkZILtlRuL8m4OueiPB8JbG0d5uuP+LvGMetmNI2niND0DXHYEKcdYcykinJCDEYKyRj9rHVGqVnMsfMTrGmxQVgNE2VUOLoYc0BWimjWtFgs8d6R67o75yglkpJm5NOU8I2WrWIM5BK1tChGoeOH0Qwdkiy5trZr4GEEnKOWI6+fLHbvTK01WsqthKv7+ZmrDspaq/yFIjRtx2Kx4HOf/Rxf+MKXePXV17h//z5NhSO/8cYbfPWrX+N7v/d7efPNN1itLigl07QtxliaprCYH/Hyy5/hc599jc997rOcnt7jaLFk3mn21LYdzlnaTmmS9lyA6/WqSoCMCmEfBlarC548ecy9e3e5d+8um82a7XZz7eu52kCIBmc93cwx6zLzrjCOwjBBxlPcnPnyiNliScpCiNsD+GbP2CEI4pzKjQgHsoF94CViMKLjC2QoRtn8kYyxBd8YTk7mvPryKbGfyP3AfOaBuofGQFOlYw403XvmCQVVMsXMGDLjVCVMXgBq98WSxUKNst7n+fDOrElKhX+jOk7OMmsdx7OWO4uOO0dzThYtlMAUE5Ij5Aiky0giRRITFIs1LU0DXec4nrccz1tmjUNKYpyUNHaz2SlFzo1YwfoOtzhBlqfk5SmpOyI1M8R4ShZCLJUOP2GsUZby/YFSnQ3oQbqPdjIwxcTZ02c8ffKYR48esd3u6nCklmVmsxn3793n5OSEo6MjVhfnPH3ymGGYtFFfwDrL3eM5d+YNGMNsMQcyNk00ziDOMBmdKzMFbMk4lMrFlOt3UGTVthHrMK6lmankSjDlEMCUCj0WkQO3m20dTTvj+P4DFqcvE13HNsDZesvFtqdUFJ5z+56e0LYtR0c6pBhTJMVIiDrZn7KC+L3rODnJdcA6EONECANxGhVEkqJqKMVEJsB+kNMYrKnng8l4r6zm123GGBqvzl5cSyrCOMUrbOBFOeCczj/NZ3O6ruXe/fs8ePASX/zCl/jCF77Igwcvc3qqBMbWWF599VW+9KUv8aUvfYk33nidhw/fZrfbXs5bAfNuyb17D3j5wSscHx9zdLTkeHnErJvRtQ3O2fq+vkLSFbU3TQpYKSVWEIsGaA8fvs1iMWc2axmGkbOzs2tfz5CFjKuM9Qp8EaPVDGstTdtiZ0tm8wXOe+IYGMdJdfCclvQPw86V19FUIEPSOaADm8clPi3XvUIRpGJAjKVrPEfLBdGqRE1biXdLqYCgAwOIvo7m9Xol17ZAjJkxJELIV0Y3Pjn7pgZ1P+j2vV2Fob9j1Okdt9WMCfBSaBzMGsui9RwtOu4s59xdzlnMGmatZxoDU9EhM0P9sYzUsog6KSTgyHQOlo3nqHPMvcWKcqdtdyOr9ZZNP2jN+wbMGsPi6Jjjuy/RnNxD5idE3xGwlKSbqnK2BXLJuKIlOT1ilIy0ZK3XX072qOMaxpG33nqLb3z96zx8+JChH2jaDqlce8vlEdY0eN8ymy0YhomL1YZd36tDLNpXaB0sWo/rOvydO0jbYmPAW6EYEDRDkJgw04BlIFdgx3VbSpm+chfq8KjHNx2WXPsb2qMkClkxzogt+PmC2ckpdz/zGqcPXqP4ju2UmV2sma83kGu5zTe4CkppmoblcomzjhACfb9jvVoRk6HgmHUL5FQbykorFRingX63Y+i3B9qlcRwYhgE3DIQQr5wjiu0SMt4ZZu31V+R1+NZinMf4jillUh6IUTNBZy1N65nP58xm88oofsQXPv8FvvjFL/L5z32OVz7zKqenpyyXS7quo/ENL7/yMl/84hf50pe+xKNHj3jzzTe4uDhnmkKFoetQ9GK+ZDZbMuvU8XXzjvlMMyhBcE6qo9JzQnt/qbIhVBLeaWK7UQclFFIM5Jw5Pz+/9vVMogwlmZr5lExKHGbNXNvRzY/oZnN1SEMgxETrFKCTSVpmL1mdE4Ipyj5iytVKFDXvyYfxh5yyyp/YGpQ5S9u0uGRpfFNBPpYs5pDBH9CBwJ5wQUkT6quXokTCtX/6SdsndsS/H5D8qurru96PxkyWgjfQOmHeCIvOcTTzHM09JzPPcqbEr84IsbJAWGtp24ZSEiYIMUSST8QqEteaTGehs4VGFGwRQ2YIgfVuVOHCKZK4/ugUoGkaTk7vcu/ByyyOT3BNS0Y58ozJh8Z6qewHIU/EOB1KfFD7A7JXINZ+VQiq9fTmW2/x8NEjSikcHx+zWB5h6oa6r923XUs361geH3Fvusdymiii/RYrhqPlDL+YIa1HZnNMmDCVET1TsCVBmKDfKX1QWavcxA2QmxYpbHdbSulZzGbMG0/jHL6ZY8UixpOlZQyjBjFkrE/MT+5y/NLLnNx/jeP7r1CMx02BYj2ubTQ7NMKsneObFqlaRE3rETGEKejsU8qEGBBTaNuO2WyOc42uVYWsj+PIOPQaHQ89m82KzWbNZn3BOPQQI6WqyoYQGEOkxESUeO3r2TQqU2OcB9siITK6gDGiDnox5+j4iMViwaybs1guWC4W3L93n8+88ir37z/gzp07HB0dHcQNfR3G1R5Sy9HREffu3WO3212q8YZ0kIQAixFL0/iqyWVr71QOA71aNlWhvv1Mo0FBHikmdtsN49izvjjn8aNHOGcVNn/NFlBOxVRFA3Pl2CMrx2BjOrrZkrabY2sZ2Bmh8XqsDSETU0REcMZiKzJBZ0e1x7SfpdN/tQ9cybWIuWDq1xYU9JIwKmaoKnqUYq7oR11xVPs+aanMMU4vUmdW8wtYz2/KQb2Dh+99HnOVAun5xwkqF2FFad47Jyxay6I1zFvLzENDxErCW+1JaSlQN4ymaehmM10wu6dXyYQpUxAaW/CSsCVAmohBG/jbYWJTh3dDLhWZcv3Wdh3Hd+5weu8u8/lcJZsroeZVLZ2CsiTEGAgxHAbzGu8VQVM50Pad0b7vWV1c8OjRQ9arNQ8ePODe3fsslgsAdn2v2VHr8d5hnWW2WHBSckWfmRo1KSVNMUI2FpxHWl2tJPtJ/0gJA+DIw6itp5gOoIzrNOMM025HGAO+ZDoWWNvgXIexDdiO4he4aSCESWmbDBzff4mTl15lefoy3eIOqUA0A/MYEVOwRvDGspwtaZpOcbdGVYtLKUTfYBBKUAYTsYbFYsFyeaSNfzGHE1wRgFGZJnY7VqtzLi6esbp4Sr9ekXZb0tgzDYa+L+QYlV3iJkAni3nVXvJkFHHXBS01L5cLTk/vcHp6StfpcG7btnTdjOXymJOTuxwdHTObzQ59ThW9uwSVdF2Hc47lclkzJ9WAGoaR3a5nu90SQiKlopmcqcpwUrDWViZ1nfXLOaneWcmXYxU1q5qmiXEaefL4Ma9/4xuHz3ndNiVhSqXC6VVeJSfdf4xrsX5BO1vSNLMqPih4Z/U8954xKu2WrWMPKkfCYWO9rFZVFeOa8ewpDlLmUHVJlVQ2RtWmylmdlIa69f+igarkK6S/Ok6Kc+C9wTl9Rr75DOqdedJhgunq53oXT/VeFcC9c3Ki0titNyw7y51Fy6wpmDSSxg0X65E0LJh5izULjFe0kLUO37R0s4W+jlH+va7xlWdKNVUkjqRhw1S1UrJx7IZEH4qqnYq9ESJOQEserZLaqu5NOfTt9o5d685R0Ud9zzDsDvM3i8VCI63W6IxC0fR8vV7x+NET1qstMWa6bo7zDav1hovVmtX5BW3XqTMRRxHLME2sd1vl19of+IdorM5NlYJFSzvtHgFoCq4YrGvI1tPnQolJYT7XvZ6NJ3hHyIXOGp3VyhClkIzDdi1HixMWZFKcNBNwhsWduyxP79PNl4AhxkCagvKTlYwVi7cGC0jJiEKjqiSCwSPQdcjxiarwWEPbdXTd7MApVyqYxTeeznQ0TaTxLU3bMJu1zGcNm7Zhdy4M64yp80exCFPcC8pdr927d19Va8UwTAnrVedrNp9x//5dTk5OaNuWaYqsV2tAaNsZzjXM53Pm8yWz2ZymaTT4OoB7LiU5jBi8b7B2v0bm8LiUEtZqn867Buc0CA1hqhRTvgIHEuM4sN1tCWGsNFRUBdjKKhK1qhBixDvHbHb9Dmo3mYoCNeTiSUUIAtgG2x5juxOa2RLXNCC29j2NOiSxlwS9NTgSqLOHtf/OAcFyYE2/7FcXBA1Aw5QYdzs26y15Ut7JQjnM/rXO4KxgjXqklPc9qf1MKnhvaVpD41Ue5EXEox/DQT1vmloq88MViYzasHveY71j4qnSzbRO6Jxj0TpOFg2nJzNakxi3A5vtlovzp0z9gqOjI7x3eD+rTADaB8izGVaKDvGWhpIzoaJLpknpbsKwIoaJKUay7dhFYYwQay2YG3NQM5zzKm3xDo64S9vr6ozDwHa7Zdhu1FE4j3eONOsoWRuvUg/E1cWGp4+fsdsOiChkNxd48vQZb735Fk+fPGW5WDLrZljfkI1VMcfdtiJy6hBubYYqdb8CMbx1tN6zaFvmjVP2aStY25CsY5uL6ivdgIOaNR5mLcFY5t7jjFEWhCgkB828Y3lyR4cLa2+naRztYkk7X5IRQghM/Y6x3xKngZICxipKMsZJN1GXsV6Hf62xYA22M3iteSBOB6ats5oZpEuBPmddJVR1OOtpu4aua2hbS+sElyMSE1I3a9vO6WNiCNdf4rt///7h2NzsJkLS4+7kzh1effUV5vM50xR49vQZfd+TMywWRee4fFvLeJ0e43Kp/3TJxbcPdEWHeykq224czjnatqkQfasoQWvIWZG6CrtuFf1YErvdlouLc/p+UDDMvvey16OqTi9MgRzTBwK4XoT1gyVlPZaEjmIM0TowHaY5wrenmj1Zx16q5OBwkDpEX0tvV+jD99mioEz4UnkTpfaT9g7G6OKTQma33XFxcYHJUp16hOqkvDV4LRKQax8q7asrVI05U8eArPa69uKLn6R9JAdlKsLo8DEqK4GW55RdXGeXUIqePXEh6oiMSGVrBpFSiTYNs8YzbzyLzrOcN5wsWqRMlEEoWWGiJWVWq3O6ztN1Kjlsa5PW+wZrgM7X0p8ySU9TYrvp2ax3XFys2fZrRp5R3Ay7OCX7jr1Q3E3MmADM5kpnlGNGvM6aWOuwVstnl/VflOLIe9xc1Sy9tTTtZXMT9FJyJFb6nb1Gzz4Du7hYcXFxwW67UyG8lLXyXNGB+tjANAVCjKSKMks5I1Y3ia5taazQ2IZFY+laZQ0vAXbiOJsKaUx0N1Di65zg5zNKC4319CFyvlqzmgIBw9FJwi3v0HVzdQyNp2kcpmYJU7+t/aAV/W6jYA8jtBUaPkyD7hfW08znLLzFeq+DzUaZn3WGUYeirTEHhunnswdBqi5SoWk7jrmDpSBTxBTwrmGWErZT1uvhBhz+vXv3tISZM7ZR5dajoxNO7tzh9PQYECVnLXv5EbSHUVQFN+831Hr/HnX6ztJ/5UVEtbIogrOZ5Dze63pNU6ivEQlToKSsPcF5RwgBpPDkyRPeeutNLi7OGUct4YYpMA0DYZowUuhaT+MV/dd13bWvZyotRizeNrh2DosFU3dMpsVMBkdHjkIYJ1YxsdpsOVvvyMXiXUuIgWkasUUwNmmZ2ahsiaUO4tc+nRFDEWEaE+MYoFY+nGjJbrvpefToIabob9j3W2KcwEdSyYRcMEkHtQuiA9mHFophu9myWa/ZbLfshoExfvJkBx+DzXwf9exLdArjbUzBV3ZwdRpary7sIY/qjKwxB09v6rT+vGmYt15nWFrHvLWkAIMBciZOIzEGVqtzFosZy6PFQQLAOUfJDXiLkRZvNTUtBWJIeOvJMXFxfk6/W3PRR5Kfc2RavHEITp3UJ72yH9IW8wXO2QOIxFytLe+HJEsthVpVd3Xiak/E0DX6fRU+XnUeahaV86WAnNaaRzbrDdv1hrHviYtFZUG//E1zzozTxHa7q9LdgZgUWbWnNck50jnBZE9jWjorOGsI2RGwbKIQQiHeiIOyiG1q/dywjYGz3YbHmx1jFu4Zz2lM3PEt7WLJrOtoW6/zMtNA3+9YnT1jszpjHHaKYmtacuNJYhh2O2KMiPNkycyP5rUfUAMEFY6tDBL1t8yX66DoN3MY3hWrv7l1hq5t8M5CiAeC1FwKs5MjMJZwAzHU8uhIYfApYVtouzn37r/E8fExXdcwjqqqexjkLFxCxa845Zx1E40xEGM8sG4AB/ot1UkCrOB8qZG7ludCCGg2r+W9GAJN4/X1kiWlyLNnT3n48CHr9YphHIhBh3THcSSGgHU6rG9r5eUmqI6mPCNjwDYkOSLLHUZ3H0qDN0F7UcWQQ2KKkXGMxKQaUamUQ88oUzAOinNahhMV2LTOVNYTXd9UQCLkEnVusWoY5AzbXUTSBbYUNusNw7AjZx0hSFkDilSRxBm5DC4ojEPg4nzNk85xPJ/x9GzLbvzkqc0+Gpv5HinGZTppRR1TaxKdhZkzzGaexXyB9R6MV8btGk3ag4xEJcwQaKzBGakM25E0BqapJ4WoiBGjGcB6dcFiOefkzjGzrsVanWKUUqDYQzNbrFTZ+EzXZeaLieXREf2UGJmYjFdajpJRCqWqPHkDdnJHa/iyT8vhUJoQsdr/kUJjwLiCyQnDpJG2COCgdFRhDXR3FLrZjOVySdO26oTQId5YkWFpT+2TdD5nHEbGYaTfDawu1pxfrBjHiXhgokgYr7T647zDxok7rhCcMJHIzjPFTEiQsp4AN+GgWtewPwH7aWIz9jzbrXm62RCKwS+O2E4jY9Sp/WxV92YKE/3Qs1lfsLl4xrBZkcOIabTElIaBKWXG3Ual3b3Hdw0lpZq1K/M2WfZzwAdaKYfDO6VCSnFP2mtrYKR6PWIU+OOthZgwRbOxnBPz4yV+1mFvYEMtpRCmQCqF5fKE4zsqVTGbzyi1r3OA3ouW6ZzzVQpctO8TQlWPVWVrdTbgvT84p30QK8Ugtuga6icghP2MYjkMomoQlpjChJ0MKQU2mzUXF+fsqqxGipEYAqkev4WCNQZX2T8uJTmuz1bhmJSFElR/zE6C7DJNk+mMo20dje3wxiA5cjJfsGhnusZWBUadaUk5Mw6WaARj98KtSnZsqqSJHCpJEIIwBcM46b4xZUsxhWQyNu7Y7XqmoFlWThpgtqnyMKJChzqLCYiw7RP54Y5hvePs2YZxTJxffPLqBR/DQe3ZmIoioFJCUqCEgG0ts7bjpDMcH7U07QyxHmPcoX58gEOjfasaMCElk2LQS45V1K0cDuCUM9t+y263pe+32sDzTZU9MFAEMeWyKViPcOsbuvmC45NALJboduyilrOUiqduKDeE4js6XjKbtfjG1uRHU+gDrJMCkrEEmjLSpA1+PMeURBZDNMLkZ2TXkip6qZQ968Slw1JhOKVCUi0jnRkapolhGGiGnl0/sdsNbDZbVhcrxkm1jHKlShJncd5CnJibQj9v6BtLzhHrG0I2DFE/l1LiXP8G4Fyj5YiUGVJkE0Y2cWAbe0IybMcdu75nnEaF+graB6j8Y+PQMw1bSuixJWGzYJInjaNCvYeelPMhoFKWcS2piDgtuRwclByO371AX7KpAibAGncI3g6ZlDWUkEhTZhwmpnHEYPHWM18srn09d31PDBFXtZ5OT+9wfHyEtZaxSmPYK+KCKSmSLqd8uB5DxNZ+xj6jMbUC8k4ksPZmlK7HIjV7ssYoAKAoutSIrhOo+GEIgRBG+l7pi4ZhYBondVApElLUoGLP6G/tQf7kuu1inBOTJReDGS2zUTjKgsfQtI7WNnS+xTshZS3ZOe8VQJUyYTYnhMJ2jPRTReDljLcFZzPWF4wvWI+2rEphnAp9L4yT0hIZ48F0ZGcpTWFKiTHoXtE2Ddk5Cko8bYxK6miXSUgUqPL0425it9mxWvUghnDTIIkioieWFBprsDmQxzVx2pLDgF+0mO4+nYN562g7j7G+ymbrF1Ym7eoU6t+2aJ0zBkuMVmliSqwEp/oY1XYJjOPAbruj8R4npkL/K7okQz5gzlTfp4il6eac3vPYdknpNpztAkOEkAtZDm7gRmyxmLNYzulmnabXJVUlzP3cQdZh5DTSDCua1SP8+iGSE6nKhEizJPuOZBwUQ8qB9WbD2fmKYZxUsbVmlqDgh1yEKWW2Y8+m3+F2HbtK+7TbDez6gWmadLah9k0kJ0q2jKYwDE75zRrHbhwR35Btwy4G8l5CIV7/BmBto3KWObBNiW0KBFNIthBSYAwDu17LGWmva1VRYymnSnKbcBYaUT0mkxMlTIQpkqYJsUJjhbbRkpFz6mS011SZ4ys7gBHzToToZQNXzwvjMM7qb1MHh30bcM2OgiOEHWm1pVDw7fWTmz55dkbTNNyZzzk6Pq6oPV+RaDphamrgmlJgGHs22zX92BNzUH65HElJEaohaG9079T0Up1PxYYLKoWONeTaS9k7PExV+G2USYJSDnx76qhiVYvVUmJM6aCOrOCpxDjFmo1cP3XUqu8YkyNmg82R0yzcP/YsXMfMWVrf6ECyN5QcsV4ptXKBFAtWrGZQZz0X257z9cRml3GScCZpudlajMsUm2oqURHBSc9h2xqaxYLFyZzlwjJQ2Nhn+HbG0fGSqcygSsVY7zQVyUBRKRXNXtWBhgnCOOCcRV6AYNlHclBWVNWzdY6lN5iUiXt0UinMLXQWnBRMDkgKiuOTjBFt5BvZOypt7hkUCizFHAAWOekm21QWblvr1aVACIHdbqtDe+icQI4JuIRDpopAU1ikHAbikjiK8SBaDsuwb6x94gv7Ya3rWtUP6lqGcc99lQ9T8AWtAUuOmNBjxhVm8xRywvgGmZ9CmigVqVSyRpX90Nc+UtBos5KnWq+s3GHSnTKGyDhNDNPAME4MFYZbKprQmioctw8qapCRS2EIiU0/1gZZBJ+03p+V7uhGctL/f3vv0RxZluV3/q56wgWAiEjZzZYLkmazmN18/+3Y2GzHOEOOkdVZVSkiINz9yStnce5zIJPV3cxmJVBjFicNGQqBcDx/7x71F9qQSsFTmHJkJlFajQ4WTSQRWf3EsszE4CuZuFy15Nq2Ie96TASb0/X+CTGSMpATxlq6xglYpCYotr3Si+Jr6+6Vft4nAteDePs3lTHim6QKxRS0bVC2QWlLjJmwTOQU4PXPU3789IkvvviC98bS9T1tJ+NocqrPtqpdey0gQ0A/3nP/8InHxwf2+562sVgrO6h1Xf87kVYFKGOr1KDAwrcftRJBX+8F8KCVomkcfd/Stg5rpXNbV88yLyzzWnenIjuV8nOXkZPodcaUcNZB8wbPfSrkpMjZYo2lsTv2bUurFX5ZmFRg3xaIihgXGmcpXSej1gA5RAyZvrXs+x0PZziPdRVSZKqlNWASaEXRBaMLzmQaHWlUpGgwbUuz29PvHXE4gRJk6aHvWUpDMo6mtTSNQyuNTog8U5YxX1ZAEu0+sowGN9LwnzN+VYJqdeKu77nbt9z0Fpssq50pXs6opnHsdy26FJZ5JsaEsU4sy53FugZrG7SxUmEXC/rFbktpqWqMIkaZ2TvnaJsGHxqxf4iRaZpk5p3lYCE9I6N8Zd7HmIkxC3s6yYE6+8RpWpl8IWtL0fZ6oLxVC9U2rZAY24YQ1xf2pJuKcJ0nF1HGIHlKWmQEmpVopKSCytJ6y/ci7PkQghjDlUZGqc7R9T3dbkeOCmfFTZhUiYxhZQ2BojK2sTgrFa7RBqM0MWeKEsVpbMeSFHpNsvmyRWzS54XVe1SKb5KgCoZQPHOKjDGwktGNpdk1KKWxThGiZ1nFyC5WaK0xhq7bUW5u6YiodUSFFb961jVV3Twxf7PaikBp12GdqG/njcmvnsVfUepacW4j221TKLfdppu2HZQV6KINyjgBYhSY54k1jIT0+v5aP/30kcPhiDIGW0f0MYQqNmzq7km+vxgj4zQzzwvf/f6f+OLLL+j6hn7X4JwkqHEcOZ1OVTEiXvdWNAXLsz6iQl/v/+gD0zAyTrLjeHd3x83Nga7rMcawLDPzNDEMA+fLhXle8cHXoiLXkXbe3hJARH279vV3ekftMQWSNRxubnn/4cjNsYe0cP/xEycdKX6HNZlpHGm7lpvjkVRyJeonCobd/j1/9eWRy1L46cmzhEyIYryJMnLvVVKzNYnWeLKNKJdxKJLR4DTGGelWU8ai6ZoGXRqSMwJc6xxRaUIBl0UBI+VCDopkMyooTK7dsPnzS3H9qq940zu+uuv5cLNjZzL4wLJashU2uHEOY0XGxK++sr8TOIdRSEIwm7+TkMZKqWdyKddlvq7CicYYnHW4xuG8I2ZZ2I+jiErqihjKUebeIcgDMsyLJKgseyq0JSMz0slnQqkIKyUowz9lIvdaYa3GGiu+OPVA20ASG7ZQOA+yKynGUqw4w2KdDPZrHlP1k0vhOiLcEHpKS3fw/sN7QsgM7UDrHH3bQkHsB8aZaRrx63o1HhS5FdlhqSo9k1PCr4ETM/PshcdiNNppVFir5UflB71yBApzDAx+YcqeoBKq0VicXApVWOMiCcp7UhB1dmcdar/DqnekxpCnM2kaKGoipkUmAsRrhWqNwVjZr26tTckFRRRl6bqTkbnAhkYrV9i/klmVVLkvOvgr30yr2lVBiB4VAqq8vmfZOE3EStze7FtyHZdZIz5lpu7dcsmsfiFMEz/++CO/+91/4/b2wO3tsaLnNPM8V4sNsbyQC1couaM0mazU1f5lG9Ov68rpdOJ8PpNzpnUOvvxQVSkUy7Lw+PjIw8PD9WuHIH5TqTwnJ9iYL7IX1G8w4jNELAqrG/a7hv2uw1rNuqyMlwuUhX27olTk9HRit9uhS8FHz7zOhNVjrePbruP9zTveHxuOe0cYMtMqWqa6gCEIiKVYSi6QBDyhVcFVewxxkhExpFQUMWtSNhhnaHorlka9wxclyYl6VudCdoriCiopdLZyT/8GYsa/KkF9c9vz1x8OvDu2pGmQJW711zHGoowlYwTFBaDBFo3Sm3GejJd03UdJo5DrDV+qa2fBWKmeVIXfOidyPMmnikBLAjHXskgNq2eeZbn/eLrwdBqIuYBuOL57T3+4QbuWoo0kS8SPRRb5b9Y8AZJfRG24jjXgmfp9fWUiM5RMR24PsH8vB55tyK4na3UFVGzVuYyPZMmqq+Dn8bhHqW/Z7/Zcni6UVNgfehKFeV4YLwPj+SJOp9VGM5lErBV+CrKTSs4RrGM25no4yaU1dCqzR9E4hzKvj+JbUuCyTpyWgTmuBCXW9NpKAk0ps8SFaZ1Y14UUAsSCa1qaxpGbhtT1+LZjNQ0Bh8kay0wiQRJqBUqgQqlodJbdLEV2WEopSh3d5SKHY65w661jUGob69X3rv79VPcmOSeKEu95rQukjI6vT9SFZ7LnPC80zYSpzsK28pSssfI9QVW4jjzc3/O73/2OD+/v+OLDe3bVVHAYRu7v78WaveuuoIqbmyN924rslha0GtVbap5n7u/veXp8opTM8bAnhHBVqBmGgT/+8Y/88MOP3H+6Z5kXcqpCys/fxjO0v/7Ft0DxhaxEqqhkrC5oEsHLiDKGFa08MVrIwt/qrEPnQlxWLucT4zhijeP93Xu+ePcl73fwzbuG2SfGOeNUYacDO72QgEs6sBZH0hYPmBjxvhB9IoTIGhU+F3zRTBFWX9i1hkPv2O8cfW9ZYsKnjVok3a0qSkw/s4Ecr/SYP3f8qgR1u2/Zt4aGxHm+MJyfuJwHCpqdbqUKNQ1FVT6OsWAb4aUYmavrF/wHKvGrpPQs8aO2cciG6tHXiqrkctWhs0bGTilI+z9cBk6ngfvHM0/nCz6Bdj3f6IZ3zZ7GaLRxFC3bEbGmqFwN3i5JOZPRKot8TtmIjrmqgW8PUiFjKG5H3r0nbagmYwndDcnKNb8+gFoUntvW1Y+Gtm057Pd0bUvf9ey6vlZjMrqz0RFDJMdA8koeIGPrNROFY1GBTmL1bKwsmrWu3LciqgxGi8SQnwnT61/VYZ05zQPneWDOK4FM1kWmZ1qsCoZ55DycOA8n7qZ33B4jTduICKxryFbm7qWINULRBmUtZQK/JjCGjCZmiBEIRUatOaFzZLMWVUIKuo6f4ZnzozfzudoxX/X51gU/z4RlJkePJuGsyHq2b4A62xQN1tUzDCPWOnZ9f9WGK6UI56ZWz6XueoZh4ONHsXl5eLjn3d0tKUU+ffrIP/3TPyFisS3zLHp7X7x/x83xKPdq09I0rZCeUVwuA/f39zw8PEApfPjwnmVZa5LSjOPI09MTwzCIZ1kdQV69jAoIIpiqwlD5RG+gzNHvDnTG4pqOw86gVWaYIvMk4+bGZFAFZw37vmPXd/SNY5wKYVlZp5loA34ZiWGGCloTmwxxczAq07IQi8LQo5RDGQNaRsxk0DFKVx4FRd04i20agrdYZ+haw2FnOB41S+Dq9qAQoYVSNCWJwgcVffmvWi/9G+LX7aCcJQXPZV7kxvv4idNpwLieO91zcIa27XGureRDg3KW4hzFNhTTULQVO+KcAFlaUkd+6OcK5/l7fT6kY4zXDspqQfBNw8DD/QPnpwuX88DTeeI8ziwhY7o9+/dfsX8vLbUyohSeX4z0tt31yyX2a0arC5ZUD7eNm5TR+rn7yDmTsCR3QCuH6e6qqaOimJZid2AcRovHTNLCV2rbjt0usN/v2PU9u92uJncLuVSYcMY5cSX98t2R8+lAXgd0CljToG0HtifZhliCjB6NwRlL3zg6q+hVpFEiuhqVYcqOx4dP3P9xefXreZ4GTuOF0zKy6EhSuUo0yTgjBBEibXTHp4d7bo/veP/uPV3qKuRbDPc2c0hlGnTbopqWYg3pIjvTVDQhgg+ZrATNprI4O2tT+Tq1E97IqhuxXJmK7FOQka5L5LhmlmliGUfW4UyeJ0yK9NZgtHijvXY41xBCYBgGnp6esMbSVl091zgKpe6h9MseHh884zjw9HTi4eGBL7/4QAie3//+D/yn//SfKCXTti33Dw/cP9zzV998wxfv33Ooauh9t8c1LVppzucz9/cPPDzcU0rhm9OJaRqZpgmtjcDKvb9C+TcdyW2HddWS5Jk4HFVk/Q0O1H8tvvj6S3b7tqrhGOEPnRfGYSXmQt/JtOPQN9we9+y7nptdzzjYSrEFjVAiLsPA6aI5nRPLIlSQrARcFUsmZSX3ps2iAG8MJiesKjQl0iVPF6RL94eGsnQ0aqXrNI0t7Hu4OyhS0qRYd4JFzqMYE8EL6lhvE5zf4Az9VQnqMi4sYyD7iU8PF06XhXHJuKJQC8Sl0KmCSwmrFdaADYUmKnzSxKLocsHpIhwSknQNm/7g1nonqShfVp4CEa1LwuqRpIrYOD89PXE5DYzDzDStLIvI2mg0WTuylha3KHud8Utcs9ObJaiuFdWNojSL0VcEYsy5JtLN4waysmA1qrToWhEqZaByRlQMVTgzS6W736G14ubmyPHmyM3NEa0V+76nbRzzMhOjp+s6bo5Hor/BT+9I4xmWUcAsrie6ntU0zOvMGjwgUv/7xtEa6AjYHNGlsBbwUZbQ9g2kZB6nC5d1Zk4erwpJlcoHE3IsKpFy4jJe+P6nH9jv9twcb6pKRkvTWJSyaNOim4RFxnVJG7Ix5NrJa9uTMaK0nX0lnovRZtFg6oOcU7pK+vyM91P3jTlnUvD4ZWI6nZiHC+s4EuYJHRY6pcRlNhfcG3RQbdsSY+JyHnC2oWt6bm9vr9MNa604CnRtJZxr1lW6m1IKDw8PUkCeL5QCp9OJn376yLLMANzfP/Dxp4/89P0PfHj/jtvbG46HI7t+T9t2KGX4L//l/+W7777jcjljjObjp498/PiRpmnY78XC45tvvuEf/uEfGMeRjx8/8nQ+M04jPjwbkcr5KukqpcT6Bs/84dBye9Oxay2zl8RyWSLTmsSQUovorVURpy2iJ5CY14WiZP2htGGYPfnhwv3ZMc2afavp24bOgSsGG2GNBZcaitYYJ9bwqn69tKyEURG1hbAiW/pIjjPzOJPjgi6ey+PHqiiRr6OmnCvQp7xcK/w2SLNflaB+vD9DWkjrxOW8sCyFkBtCcRAN65RxKeBswuki6s9G07aRrousseXQWjqraHTB6oJBlH23/UvJhVSqM+kL3bJtQSs2FKZK8qxM48QwSDW1+qp6kAuuaWi7Pa7pUa4RiHnRpKsOWkW9XavctzEs3MZwGctoDSHJ8rIkId1u8iJSvVR54io5IWtk6SRVDCgtu7mcCs5Z9vs9bdtyd3vDzc0NNzdHmsaSYmR/6JimmXmZ6Pued3e3OOTrpPMj8fIklWjbMZmWi7I8ns7kYSCnLBWzMSQFPkZiLKIBGBI+ZHSK7HdvkKDGC2Nc8CWKUj1bJS1jz2QNXsE0j/zxhz/Stz03t7e0fc9+f6iKELoCehp0IzJGzhhRLdGWFCLG7YSnFBPUsbPRIhKriqagxGW3qmkYI5yd5+lANdPLiRAX5mng8nTPfDoRphEdI02F9dN0MgL+DewM/rXo2h7vI+fzBa0t+92BFNMVfSieWC39bsd+v0drzTSJ2r4PkYdH6ZAuw4W2bViWhcvlwv39PdM0st/vuTkeub295e7myLt3Murb7fa0TYfWttrD/1fW1dO2DR8+/MT3339P27bAF+z3e/7xH/+RdV1xzvGf//N/5nff/RMxBXwMkJ97qO0KpmrN8drRtJpDp+lbVblkkWmNzLHQtBaUZ5wGwiKcJ2cd1hjmdSWVXEFohvMYefIDD2PL7Fu+fN/z7tiKbFourL7nsiTWVUEqsoctuZJsE8s4sZjEgoW8UJIn+Yl5OBGCEPR/+v73KFWu3lzbGa0UaCerA6UNMeVKVn/jHdR5CZASOYBXHbl1KKcpTc9aLCkUTAki4qpy1ShTuCXSLJ5hWjl0hpvOcWwN+6Z+U3XhXDLVCymQYiJXtMmyRqYlsnjxSOr6PX3biFiqaejajnn2LD5xvqxcpgBNS7O/5XB7i2s6Obh5xh6UKjlx/fUbLaGyMSxRJGMW75kXf/XFsZUrdh17apHZRz+TnGsb9VzRVKSYWMMbcXpdVh4enlDAbicSUSEmlNY0TYMlUcYT52lhvIxM5wtLlfRJSjFmzZgK52FknCZJUAiSzWmNVQWHwIQN0h3bTrM/vn6CmsLKWhJJ87P3ewvrLG3XEogsYeH+dM8fvv89u27PzeGmuuY6+SvaiE1DUcSiSMqC61AqkBBPo7KuQKwJSlOyweSATlo0DGNCWy3OpVd/L6k2Vb0HYwis88RwPjOfT5gYaBS4Ru5xEK1G3iBBKaUJIZLThHMt4zQxzwvLstJ3bbWmF3qI3RB9CKIvpSS2Lx8/8nD/QN91lJJxzpFSYpqEirIuK+MwcH7ac7lc2O/E5BEUMSQeH5/4+PETOcve6ne/+x3GwMePH/nmm695/1681EKItG3Ll199iY8BHzw+RJZ1Q5bKtX+GHr1+/O6HkccnT+MMS4icpsQUEPHhphPUc/IMw8QwzMQgHM+mbel2LY1xYBp8McwLTAGS0twcGv76657Oyj21rpr7wZOeEnkBX5CdewXlyNhKHMmDH5kuJx7vH/j000dA3KjFey6yLJKwhFYg73O/39PtevFGWyPGKGzzxjDzydcqvjQoZzEWgdEaS9RW/EByusog6aplZIzHzpbOGcbGEA8N+qanMS2NMz8DLOSciUHkUXKBlGBeA+MSWELm0HTs9jfcHQ/su470/gNfffU13mfmNXN/mng4T2TtUG3H4fadiH1WOPbLm/OlJ81bjfiCUsQ14OfINC8M48w8TpSUJAEYWyH8ojigramqHAIS2aRfdB0dlSqPTwVbCMHR84NWrMvEze1B7LLVxuJX4Gf8MPLph0/8/vsHHoaJyxpY14APK/OamENgXT1r8FdkmlKSBBvnaBtLaw0Hq7gxhS++vmX39Vevfj2XHIlKLAdq1q4kxkoydgq1awHFkhYu04nff/8dt8c7vvjwBa5x10KgKEFchSi2LT6kqsKhZewaV3IJlGq1rZXCWyMeZBohiqZI0zSUvXS1pVTuTakkc0TJ3i8L0zCwjAOdFk8zZwytqxB2ZLrw+qHw3kOBtl1EZWSamOeZ/a7HOdHgE2UIcyUf62omuCwr95/uub+/5+bmeAVHbKaF67oSozgWrMtCjIFL24iUzhLEXmbxhBBxzuF9S0qR0+mB7777jm+//Ya///u/59tvv+Xx8QHvV25ubogx8nR64nwZWH0glyhAggrCeitq/n/9fsIpg1UKTCEpTSiWfeNwTcGaQI6KYVr44/cfGYaJGCJffPmBr775kua2o2k7fGhYgsYn6ez7ruHdTcPBFQyREARZ+zRnBi/owbwpdWiE7mMiyhRSmhmHC6fHRx7uH2hby24vnfOyrozTTIiJpmnqOFfRa83ucMNut2edV4wRqaY/d/yqrxiLQmEq5l3GJlKRiLYUG7eJQkYOhQJCqM2JEBMpajqnOcYs6sxK15FVNSkrUn2JkrEiZdlfhWxItJjmwP5wx+3dLXeHPZAI3uMjTGvBPo7opwGfC1kZbCuAjVwEVVXrh+fd1hU592e9rv/DkYuu3VNgnGYeHx64/+FH1nnGKoPVBussxgmSzDqRH3GuEahvRVNtH9rKnu30+MjDwyPjMFb1bNmPGC1wcVXAWUvbt6SwksYL88M9jz98z6cpcElKRqa1ChU2fkUBbdV/BSAkrQkBVM74LAS+nONV/PY1I5BJVXFfsVXJ6hlmbBTKaZq+VDPAyMPpkfunTzw+3dN3Hc4KAICyIes84zBWjtgsZoaVGF2KouQ6rtmuixbH1+AlQe0Oe96/e4/Wuu5VREIJZBxIFrO34APLsqKNJlYOlLbm2i2/xS0aQqhVPFWVITIvC/MieoZKb4AScQp2znE8Hul3e/aHA9Qi6fHxkZubI957QeiZDd0oygShZLzXLPNcre4zy7IyDtN1vLRxyUIIzPPIOI7My0zbdez3ex4fH3l8fKJppQAR19yOYZg2jDRcUX1XUserxrvjDhG0EGSpIaHiii0BFSMhrqzTwjjMLNPCOq9EHxguI/1ux3G/pzk02CJHdykQQ+H+NPO7HxJ7W7AqUUriccqMSyEmRVEaowsNCqcrfbKkqnWIGGbud+x2O5QWo0xjNI1zxDZhzHMHZaycSc4Z2dmWjNLg7Buj+Nh8bdTzNLe8OAa2KFs1Xx+qAghQTTyj1pQJWcQHqZwQ+RrCqZIEIskpFk0qjqw6OVi6I7vDHcfbO26Pe7QSGZolFOxSmFXHhMOESEjC5ZF784X774vk9OKb+5WX7s8T2zXKubDOC6eHR77/wx8ZTydUFl8trRXKGhFrtQ7jzFWBom07ur5j1/f0fVd1uwo//vAT9/f3LPOKcy2qiH5i6zQ6idJDcQ2KIyZ6yrISpwF/eWKdCysNIZfqFyV2587qq43ElU+y8Xq0otKeBV0IxPz6PKik5P6seWJjZlIRxvXQVzTKoZVinSva7PzI49M9tzdHDoed8PdKocRIXFemy4XH0xOX6SIjo5xQpV6DjDg4b3SJ+q5uagk3tzf4dcU5KweA0jhV5acwGO0wykmhl8EjVgsJgalvckkvoK2vFsuyCu/QGCgCLljmmWmcWI4LClhXEbX1XkRgjzc3fPX1N3z51Zc8PT4wXM6cTmc+ffrEuq4yVt50Nl8oaeQizzIUIdrG+AKevyF5xf7FBzGWLMA333zDMAxcLgPDMLDLstvb0KmmvvYrJopavLzB9fwPf/eBJWR8FIpN8DPLeELnFVcCJa6kkNBFs2s7bFHEJuK0xi+eFHKVhwNdIiqL3cgPnyKXcaYxYn+EAp8V51WzFktxoq7faI1TWZymYyT4iDWa482B91+8Y11GlnkiBk/bONoqwxaqYLTWmqZxlaCtsLqAUygyWv35d3q/sieTFhmoT/u1LNl+8+efy/MBDKoyloug01StIDctL2p1LjIIpAI+F0LRYFtctwPX0u2PtP2eptnhmg6lChlPiYlQPCELK3oj4cKzI+Sf6pIKP7t3Xz20ps51xdNG5ImSoBXjxhHL8oQaXY3hxOJAWm4n3jZtg6sPfiHz9HjifLmIJ1bTss4zOSz0OtHnvaAtS6QshpSrark4zMlIDDGYxNgq5lkfaK22tRfXeX4lWScKAUXsNEEr5vI2CSrL/LZ2HjU9vbhNlRZ1DADvA6lEpnXkdHlinkVEVm6YTEmZHCJ+WRgvF+5P91ymgRAD1L2TgiqtJQhBlMIoJb49MTEtIyH6KgW2Q6EwWoQ1NZqm7djt9xxujvhlIvuZtRQ8mViRW9c94yuH976OgmXUGGJgGEZO5xNd17B0HfPqOZ3OjOMo9hvWcnd3xzfffAs5M4/S7Tw+PpJzkXvViYj09r5sI/dN1zNGQfLKH26K8Nv4Wkby85wx5zOPj4+cTmesNdy9u6NrO3zwHKeR02Un3nQv6milxLivda8vvvu//S/fMscobt8hss4D8whhNuSwkIIh3nSEd3f4bxbCuhK9Z8lJkMnGsK4rfl0oPmNSh8sBv3akLHJewLM6RIFiwFmxmjVkdImQMikkvI4oB64xHI57bt7fUR7EKqftetGmLAXvhQcXU6rcVIVRQiK3ptJ31Z8faParE9TzT18mJ/XPfdbz71zz2Xan/JzzVIqQVcUcC1KGNUinVUyL68CSabsdxjUoa0E7ColUNGvwDPPKtC4sIVT7dBkTbmS9X06e3yopvYxCxFiNtXVxrqBpGmLXEX0geK5KzqoUlmVhGAaRgrJClr1aF+iaVBB5GB8COSusa0SgM67ctpp9uaNpNCl0hFgISqxOfC4UpVFKQBC6gjBKeYZql5wrTH87KKSyFQNKyNZRGkdwhuUNElRWz3r28KwesL1kyav196x0U0qDjyvjNNbuKD9TD0qhJEExhRAYppHHyyOLX0Q4doOOl02nLEEll5ecKamwhIkQF/qu4XjYY7ShMR2qqTqH1tLsenbHW6Z5YjhHppwZY8KljGlEveJ6YL9ibMADUwnHwXuGYcA5U43/HPO8cH//iWEYpFNspbu/OR4473q00azrwnAZRAuy62jaFuvcFaG6pd9ng8NEIXPFAdUz5HqEADFF5mXifD7z9PTETUUBWmvrGHLh8XTCOvuzo0opjWsadrvdq1/P//U/fMEaE4uPLMvCMjuWyXA5OR4fTqyrOEBoFCZncgzE4LksnksIVWoIrA7smog1ioAju55iWmLRxKSJuQhiGa5JS5ROArl4IpFIxqtypQS1XcvheGCukmdt23I47NFaMc8L6+KJcXtPVHU0VyQlnfBvMOH7tQnqZfzKV1OuiO46q6dmXSWWwjU5lZxJRZZ6cyzMASIV9qvK1aV3U4PIWeETzGtkmldZiNY3J1MlaLax4b/9m/3NYlpOqGhYfUCrTOssXdeSfKjkx2uqwBhDTgWlJkkKdVSqlGij5epPBMJbyDUpFxXlEHm68N9+/yOX04WD1VjXELsdfWM4OMXpvDDEwhIza1ql+iqye8j1fSu5qqyXfH1DFeCc5bDv2R16Dh9uaA4t4Q38oH7Jcqujfvn1tSDPcu8hCv3GPCtix4qg3MbMuRSyUtimodv1tH2D81Z2bamQVbo64moUtgjoR2ZSImScfGBcznz8+AO9aykRVDLsDkdc0xKjZ5xX1gxBOdbiiGGFMRCYSVrJw/8G0jwCk5duVEANnoseKUqSsejrLfz08SOXy4UQI01OzPPAPI9Xy5ZcHZ+7bocxhr7f4VxbVR+SjOCrJYwkI7GcQZdaC5drjYveJjNCI1jWlcvlwmEv0HRjDClD3+/ouu5qpni189AK1zh2x9f31+oagzE1KaBptGPfHUhh5g+/H3l6GqpKi6U1Gms01hm+vvuSvz2KD1fJmdUH5hCJyRJLQ6AhFkcsmpAUPikWXxjmwGWNDMHjV8/FT3g8nQ44pVGdqTJVW6ISKTpeTKA2zpt8SDGmgbaREWoQdBzmLytB/Uuh/vuf/onsoGomviaoLHYTqSjWrJhDZgqZUDRJGYzKFKUF3YcS+Z1S8BFmHxnnFR9SrRxeSNKWP/0SXtqqv1Us/oKOhpgK1kDXSmWXU6ngEYPRSeDP1hJTxtqhHhx1dp9lNBJjvO7aVAWfKC1E3pQL52Em5k/89JOh1WCsha7juO+5O/aUGAhYQkl4v7KGTMxZElQu9VpWQ8Wqm0gFn+x2HfZ2R7/vONwdUTvFzOurb3NNUL9QC9myFZvMkBxWrqIQNfqqgC+JqSanWrI3bcvucGA37ehDRygeH+RgNUbTNBZTS8iMUE+EPK1YSsCPE/f3P0EEkkGVhuOdp+l3eL8yjSPn0XNeM+egWNfCEGamEMlKnKr1WyT8675W7DTmGWIKhCDGg6Vk5nnm0/094zRWu/DEPI2MwyDIvJSrprGi63Z0fU/fH2iaTpK8IJhkIuCsjExzlJW03l7Dc+Ve1IaaFFSrr11dikk6UmNxttD3O3bdDmts7ZzluVBaY5yrYIrXvp4yYrNErBKXat3veHp0LPPC4+MTFI21hq619H3Hbtfz9ftb/vbv/o7dvscoRFUnJVJR5GJYfSSETCyq6v1pTmPix4eF7z4NfPdx5WldWcYFrxaKi+w6hzEt1micVgSjsRUVDFR7+QhGi8ZkLetKllFE2zb0fYfyQaTZfoPr9RslqBfxL5z/m4J5iZGUYu18IGRYUmH2mTUW6aCUFuXdAjFDKpqEYUmJwQeGxTP5KMvHut8S5I/czPl5G/GzF7WNf94qUb1rOkzTEEwhDYG036GzonMtT+bMYldx/1QKozSrX69jt5TSz17/hvbSG/hEG4xrsK4RmLoyBB+JBGYl7qRqWkkhYErmyw93fPvNN/z40z3jd39kmQYhCGYZ5el6gOz2PW0rN3aKkWkYiVEOrHldmJcVZyzavP411dcyWyxIBHdqRVekCui+NNBsVYPFsbN7WifQbgWVjycoJ5C9yT7t2Lc7RtsRzIqJkvmMUjhlMdeCIV93KSUpGkTFP4fM0/0Jwu85PQXawx266cUJdp5Z5pl1XfA+kFPEqMS+15zPI3f7jpv+9XllKcWaiAreB5wzuOjq8yoqGfMiyuQpxXrp6/a57pZjFAJ9M7ay22ga9vs9h8PhKl3mnPg8bRYYMuqjcgLLdUVQVAE02hoa17Bve4Hxp0yKiRQSznV0veWuvOPd3Yn9bnf9HJkCZOZp4v4NZirTcKYkTw4BHwLKGJpmx83NDd/8zV+Ba5imQAwCEFlCIVxmhlGu8a5vcJ1DG4vNYsbatj0xeFEtqVOXnDNPl5XOJRav+fFBxs0xG6wypByqv5/l7vbArmtwTxeGcUYbTc4iIB1DqG7ciTX4SnBOxBTr82RJaSWk+Jucob99gvpnIvNM5itJREpz1qQEa8wsPjH7xBqLgB4QC/FYxLZ4jZklZMYlcJk9wxJYQhQVcwQkIQnqGWn43LL+5cSd6zCqwZfMZC2hbWlMg9FO5JyURmsR2NXANI3Pnd/1gZUzYTPMk+5AFtvONrhWxDdBEYInRs+SvUj24NEUOqv55uuvePfhA8O0kHNiWUamcZYEpWQsotWOxh04HHoa66TTmifWdWUYR9yTpd+33LBnt3/9JbRCoeu1kUGwqi6kInC78cqsFSPMoAMOR6M7OteKQr6CnBMphusB6epSvbctvekIesVW8IhWYDHoqkySUyLFCmfORaSTtKhGT8vCMt/z8WFFNY8k3TLNK8uyEjdwhtJSUpVE38L5MvHF3Y6vbl+/g8o5X8VXlRI1Dhdcda/1lJJZ1pW17u7k3tzU+Z/3lt4LWnIDXfR9z/6wZxguxOiv1h3OuasWYooZv1apolKuwi+bkWHXtRwPR/quwyh9BetYYzCNeJ0dDwf6tqO1llBifT8zfl3fRCz2xx/+iM4JUpRpSNugjYyZP3y4A6U5n2fmeSX4KEakVSFflYgiYpRB6QJGcTx0HI9HclxlJ1q7xVIi+w6m2fDDPbQ6y6h0w9oW2Rk3VrPfdRwPO1YfsM5UubTCssxMKQmCtBTpdovsw2W/XeSapyTn92+gxvObJKhfYvte/t7GZcgpkVMkZX1VM48FfCpMa2BcA2vMxE2mRCnhDKXC7APncSKkxDDOPJ4GxsUTKzpw292Uq4HfNu7bXgnXJfiVpPtGHdSt7SgJVArYtOJUxPUdPrZ1Cd3Q7/a0TYNWMF7OmIpO25LUtYuqCQokORktVuRt4zgcD7imIefEXNW8/erRBVTOJB+ZxpnHpxPnQQ6S7WHeuGJkjSLhtKKpsHKVZdznF8/9tDAPI2Fa+Yd//Bv+3fuvX/166iyzfYHAGqx2ON3gTIPdBHWNaMhZY4iqocFhVMOu7cTFuQq9iqW5HBApZVII6AQtjr3paZyuo9iCzgI3lwNFYSNiTlgKuli0Bd2K6+iyKoY5sE4ja17x1fH1+pSojRKhWFOSYm0pDOPrH6jb7mmTbPJeYdYVY80VHReTiN2a6q9UqqIJNbE3jcOvK8s0M40jyzzjrOW4P/DUtPh1rXsQUcrWWtG6huQSi9ICoHpBQEeuEn3bcndz5NDv2fX9FR3Yto0AMGLAUMe42lBUqq+PK0rzteP/+N//T7rG0FRkZtN2HO5uRW9Rad4fW246S84HCgrXNHRNx7u7A+9u9jhbIIxQMspYVO4puaWkACWTtJGEQk0mKUKSZ7ZzBlMabF6rlmdGI8VYCGLuKJMY0FpMZ9dluYKKdJ0u5FLw68pwGegbi0meTkXQ/z9JUL+MXx79mwdUTALNpdoIxyLd07QEpsXjY9rWBmzIv5gy07KSS2acZsZl5TLOLEHQfFeE1oaHfwnPLS9+rDfpNUn91hfhnwmHIaRAiR6SmNKVbMnRE1YxXtR1qWuViLCq7QGrsQ0vr9uXyk3SlVjnnJV58a7HGkPTGnxYySFCFKmdaVr49OkBHwMPj09M00KKqf4z9d/Kwp3wy8Jihbu2LivJh2v1WqKMslrV8P7w4VWvJYDFyoGkakWuHVY3WCWwZlUXvab+p5QScVfthIRYXUjFsThfO6ngPes8ieV2UXTaYYwSQ7xqjbLpJZZsqh6fENajpkLPNAlF9JEpRKbkmWO4FmCCbK03fD2MQ4I1iFP0vLx+gtLVePHqSltfqtJUHpN83vYcqdr9UApaadq2pe86hvPAPI5cnp64HI9opTjuD+y6nnWegGo5EiIai9WapnJtitai0l8RfqWIw7Qzhr7raJsGZ831me6aBussl3NgmWayD6gsZn4iDPDql/Ea/8///V/Y71q6TkxKm6ah/bSj73varsc5J4wSa7DOYZWl7wy6RNZxYCmenD0osE0jSjAFqJxD04hyOSRiDOQYUVncBvauUEQvGZMKzggSr+RMiIEYq1uBhsZB9ImoA2gprKwr1eRV0ZhMCjPJtzS2YMQI+s8ebzLi2/gMkqQMKgv2yqfCEhLjEphXYesXtqqpgBINuXFaWNYVjSS0NeYr92lDsW+9xPbj9TjPL+DDv+yi3iB8CHi/sqwrqSRC8IzDwsOnM48P96SsMMZBAWfNddyyPYwvDdg0G6weoEhicxZjjVShCrq+RWkYh4GwrCzrJNboy8owT9gfLasXA0gf8wu8pSxN19nzeP/E5XSR96YqG7et43B4x4f37/jq6y/59m/+mrsP71/9ejrd0RpHay2NcTIOyUoSRpSRaFFAyhRN7YwSytZOPkVyjFUaSyrQ4D3TONYR1SrkZaWxylwpDAJh12gEbYqyFC0IQIrQJlLKrDGxRlhywZdE0uqaoORdrFon9R6WSYBljqJq8drxsivakpTSirLt2Op9tRFur1JHSuGsY9/vOOz2fCofGS4X7j99Ytd17HY7bg4HDrueaXCs60JYQ+1YHV3bCapMi2qNNlrsaAoEOUBkx1iK6IPGwLpMrMtM04jayjiOPNx/YrgMBO8FXm5EL1Aen9fvoB5PA7NfaBojhqJapj65KGKUwlprcI0UlcfDgdvjkf2uY982ggC0haZt6Pc7xslzHBesVjhraPqepm2wRlHCSo4BnSJdCdzYQGtkbJhDYdeKtQeKOrINlCKqEV0HVlsOOynEruanWlfRYEvXBJzx7FpH01jsbwDje5sdVE0IeRsFUIm5MTP7yLwGfIiVX1P/CoBCNLpKuA7sNiFPNkb6tdviOt57zlA/75TeFr8nsYbEGiI+bcmgENeFuE4QPDkWlvFMSQGlNeNleF4cw8+S6y9z7LYz0FoTgmdZtCgQK+FaOedYFLJcLgmf4vUaFqR6LpVLttUJMSamcUZGCAlrNPvDntvbI3/7d3/Dt99+zRdffODrb76mP7w+z6R1O1rraIzFKlPN1UpVyd+W94qsQGmxMgk+QoZghXMSoxg5UoQftiwTw+XMcLngV193LRpt6lRDZUBXAqPdsPlkXS21cyaHxJo9U8jMMeOLWGhv6hZw3dg8F2VKC1WiaAFtvIEW30tHgWuXVHdrMUVKBeps+oW2JqdSgT27vuf2eKR1juhXzqcn7vuOxn1D11VDvrYjejEajFkRAKtFldtuahtWi7KMHAKoUkghEpaFpgiiNyyVwFr3JPM4Mo0jOSWsEVsQV/ePb4XgLYgrQyYhq52Mj5F5DlyGIIaYFRnqWseub9n3Pfu+Y981NI3GtYauawVocrzheDzSOkPXOvrdjq7vaBvHOAc+/TBweTzjhzNqWbFtRBVPKh5bBIWaUxbrl5SgJJwp9C3ozqLVjrbRGCvvq0xlGvr2wO2hYdcZ+q66pb+15fu/Fv+cdMjzhuQZrnCt89VmnV1Ygmf2gTXGn+2enk9eGWPFvGnBVdWIF191k7fZ5JbqSP8Kid72NvJz9czNeqsOKoFPgkjUtqFxgc5FbrqW9WbHOHviOnOeRlYfeHo8CVrqxesVNBV1vFmdTdWWoGTpPM0T87oQY7yqarumwdSH1WhN13Y0L5jjy7JcvX0ElVYRkXV8mgsoZ9j1PV9//SX/8T/+e/7mb/8du73ILpk3MNjrm6NAjZUsekuScbJkg0ocVkrm71WwNMZ0XfAv80zwnsZaSk4EvzIPF06nRy6XC6AwxtK4BqsdWEvJoJTBGou1jeSnlEgU8fXyK2uYmULisgTmmAhFkX6RmF7GMxBGLBIyqjpVv25svk4vJYe0qsKjVxuYwoZgyCgSQnnIKXLYH/ni3Ttujwd+MEbkvB4fubu5oWsb9n3P8bBjmS5ELx1RjpGwCEXBakPR1M6s0BjwMYgu4LoynS/YPbTKXkex8zSircX7FaVgv9+hSqHdxoHOCYrz1a8m7A9N9QUThGRKZVOQI+XE6kWHcPNckusNzmqcFV8n4UZZmsZVibOevnH0XSMJv2/pu5aYFKfB83BeeXyaSDGwNgprRfZo7Sx+7nBu04ZMGJVp3Gac2NI42HVdBUhZrHEiIeV27HcH2qbDWi17//Lnv6L/0wnql0npTyWpLRmJyrlccL0dprW9DaWw+MCyrnJglPwCiVeev4oSqaRrZ6WuKnu1//hTEpDXLPTiFZWf/fFbtVMxKxKajMEYR+Na9n2ipD26FM7jxDCtDPOMzx5nCsdDT46bnXU1Dcub2oMR/TZjnlvzqnaQswhwait6ZwVB57Vdx/Gw53g8CgcrJ+Z5Zppmxmmq6tULMSRKKi9m+DJCPN4c+PLLD3z77dd8/fWXGKOvUO3XDqs7qeALFYhTyAl53de3vy6Cq9dWqkoYpfozbXpvMcWrcsflchFPo64TZ1tbd3xGkrbRFmtbnGsoRRFjZs0iXeXXwOgzwyq7Jx8hFtmFlJ8lqbpPVM/SPtL4yw36FrfolqDkZdTXcQUWyWu7rikL5CJ8mWUShYebw5H37+746ssv+fjjjwzjyDQMTMPAYbdjv98R/S3j5UQMgig1m0J/dSDW1dJDicIpPnhijLTWYorslq67lOCZ5wllJEHlIqi+tm3p2vY6Odg6qdeOrtOUrMhJOIaxAmRyzvIMI3u2ECswJyWxCpE/EdSukt2f0Vq+F+foGkvXOLq2uX4obQix4ENiWQVEMTtD04o82mHfEbwnhhYhSRSsFRNVlR1No2gaQ993NK7FVKCRsw2ta+mcOCtvR+tvIb35Z3mH/uXOafucUpn7VJ8o4aFkZQhZYOPj6pm9YO3r6F4+tCyctwQkg5NnVtPPt021V3sJfKgyQfLFtspU/SIxvc3mVFUhUGM01jq6LmO1onFSHd0sC+M0M0wz4zgxTXuBoIYocOaKMBOuSSLKeJ5SNNpZjIbGWVx7K3uQkuuhubLMCzFFbm6OfPPtt7x7f8dhvyflyDJL9zRNC/f393z69MBwGZnnpVpXSBXW9R3v3t/y7u4GayD5lagE3v0WatEkqe5LyZUEup3v6lqfKGkvpcvOBVU0zhhZrhsNZGIKLKtnmCbO48gwT8ze47qu2p5olNHoolDVWE4OPicFVMr4FcIaGZfA07Bwnj1z2JySawLKLzp91M9APrUXRlTjBXLx2vHLBKUVZKWuCV2VZ3eAcu36Co+Pj/zhuz/w4e4dH/7qr/l3f/1XDKcT3/3h95yeTmItcjjw4cMHetcwXs6U+m9Zq2m7Dlu5ao1zNE0rRVYlDKcYBSTRduz7Pfu+pyik450mMIbVe0IIslP1K0WJKooPAefkkH7t0EoSkw+x2tlEQsyENVJKEhPXRtO4KjGWXJV+Eli6j5FQ+WcxJXxM6HVl1ALwMcbKeHCTQNvuq1K7UGto24aub7ldFkmQUXaqBugbg903xGaHtgVtNba6JBgrVA1x9S0k5ckpUpIi148/d/yqBLWhdH5t6NqmGqXq3FU8dhYvPJ+QhM80Lb5KFSU2eR+R6kEOmGuSuj66NX7+mq7jr/8OSv7L7+flx9skKKO1jDGMQTmHU1CcJXcNcZ/wvmdZdszLwjIvLKuvHk+xwn8lMcmSMxJiIqRydebFCDJM2QYNpDUQ48q8LILAS3VM84L8u5nvdRX6uswzUz9Vzx6ALH/eNRwPO+5uDuz6lhIDyzRWmKrGvMFISqSt6p1Su3StLOjNZuFZvFhR0FrjtKHvXbUPd4CMOMd5YlkWfIxgLE3X07QdTdPK6LRyTpQS5r8xQOWbqCIjm8UHhilwGT3zkompFllqK5IUVyjcNqqur1PsakrdCWaSegNtwxc7KOA6dlKUZwPFreDbhpU5czmd+eH77/ny/Xs66/DLQtc2HHY7kg84Y7Ba8/7uDl29ypxWDMNAyYnW1jGcgsZaWusoFFIWwjg1cfWN7FuM1rLLGUc+ffxI1orT+cS0LMzryrTMxBgxVmDTtnKuXjusLiQSqiRk7iwSbyI1VEQZ3G7CuLJzTxFiMmJhlOs+ONdzLsvnRL1NqSIg56zRGmcEZu+cGA2qagq7FRglCw9VGVEm7xtFZxpy2UlRpArKCDdKCyeFopLsrIpCZU2KmvSXkKD+LSGkOumcrJLDMoTEMC/kFJncQsyF2Semea3Q8p8NPACuyal+0X/x3/zZSOLFR51I/Hza98ZhtfzPYMUL0lXpp+xIOZNSS4w9MYSajGJljef658JRCTEQ/HOiWtfAMHseLyuDF2nIUDQxZEJI+EWMDEvOjMPED9//wOnhUUYfRpbSpo5AYhRJfmsURsvFa53h9tDz4fbAza6jMYV1uoiMSx2fOPP6CUrVZAtc7dt1ruPfInPyXHeOGoHaNhZujh13tzt2fY9CbCaGYWQNEW0dN7fvUEqxr1yprbrfdqFKIZb3yV9NDf26Mo4z4+CZpkwIkAXsLNvU8nx91IvkJKuyIp2efFPCqdKvn6B+DsKRsbDeLEW2vVR5nmRUpwfWeebTTz/xf5XCT3/8Y9WRTPRty+GvvuWw3/Hu7oYvv3hP33VCGO1a/ul3v+NyPkMdvQphNxF9rOPXhFLSZTml0I0AUiJS3PqUuMwTPmfOw8BlHBhmGVNLcS08TEEcvn5RarUiGw1OaORGi8SZ0dLFxJjFHyyLYk6unmWxJFKJFJVFikgLEGWzvtGbBc6L9YfW6qrl1zSWtrof9F3HbtfRWQtZLDt0URidcI0CGkATsyhG5G28mDw5B1Kp4gHGUrIhZQGw/BZiR799gtrAEUWUI2IuLErGFetqsFaJYVnKLEGWhhtl8d8SP5+X//q/99rRGnBKUbSR6j8rNviuqJgnUrLk1snPcyTFlo3wLCKxIj8SojgRxxBYlsA4LTg74obAJSSKl92KyQFHqdYZCp2FY5GXCaNMVVe3lVWuSTFgiRxag7vp0UoJSfLuwLvjjp0zmJIo0VOCQqkGrUWK/7XDGn0dmyqlxDcHOTxz0fXBl+umKXRO0zWGfdfRty0GRVw8yzSzjBMqZXZNh3WNJF2lxckYXoyhCzlVw5gKhU45s8ziRjqvnpgKuWxI01JzqBDK1fXrqeu4bAutRTvQKFMNrl43fvlcbKlIa4WteyL5ta4Hoqizy+2lmMaRj1EU0bsqLuqMIcXIPE3M00TfdXzx4QM5RsbzhRxlOhBDqPsX6vNRWweuvabsnbxnzSvnaWJYFpJSQhPY9q9GVAxjpWhsBfC/YRj0Px2SjGX31MVIrIoSUlSKWnhKEGJmWVMd7ypkbq7RSQSyVd3LX0m0ehsNPxc7RmkaJzqRXefou5aua+hbAVHseo3VAZVmSgigAqXEyqO0oo6inaBUtyKpFErd627i+kkaud9kS/qbJihFQVcbjbItoUsmZ8Uaooz89PVWkzenyAivvHj4/0cNmn9Z7b38vZcjvpfIvbfmQXVa+DgyxzGULMvekhXJyM0o/BNFyVqqFatfZHC5LXJFq6WUiaFyd6qBYduM6MeRsq6kHIgl0FtFqwwULSPAuJJjNU/UiqQV0ShMHRFaDbc7iznInmXXdRz2O25veloDOkVMiphssSSs0mJr/crRWFMBIpJcrVJXleWURSbLJ7kHNYW2Mey6hq5raI2FIIrdy2XEDzM6w7HtaVu5lqRcuXTVRbZIpV/ys2ZcyRBTZlk84yjaejIeFyvTzc5EFKOrJxo/T1CbBYXW0DorArf69S2KfwnHVog9yea2aqvVuzUyQmrahrZt60GWiX7Fe48CnFakaAhKMc8RHwK3t7c0TcNXHz6gvvqKh4+fWNeFcRxl3JwzaLElkdFi5VgZgfXnlPExsCyeT6cnTsNA0pqm77l9/462b3Fdi14W4V2+2E2rN3jmD92RgnxfseoZilOx8CF9TISYmZeIGlaKLqAs2ipxxs2gsijnPwvnCklWAM91f6kNpo5Bu8ax2zt2u1YQfq2jbyxdq2mUh5RIGxitSCdXtNxzTmusFUpFLln2Xqs0EtGK2WyuMsa/xdVUb63m/Tk+x+f4HJ/jc/ypeP2S7HN8js/xOT7H5/gfiM8J6nN8js/xOT7HX2R8TlCf43N8js/xOf4i43OC+hyf43N8js/xFxmfE9Tn+Byf43N8jr/I+JygPsfn+Byf43P8Rcb/B+H2d+1mRJg8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n)\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_failurecases_basic.py\n",
    "# Append the folder that contains the\n",
    "# helper_data.py, helper_plotting.py, and helper_evaluate.py\n",
    "# files so we can import from them\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../../pytorch_ipynb\")\n",
    "\n",
    "from helper_plotting import show_examples\n",
    "\n",
    "\n",
    "show_examples(\n",
    "    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c734323b",
   "metadata": {},
   "source": [
    "- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4931576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()  \\n    # normed colormaps highlight the off-diagonals \\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()\\n    # normed colormaps highlight the off-diagonals\\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\n",
    "from torchmetrics import ConfusionMatrix\n",
    "import matplotlib\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "\n",
    "cmat = ConfusionMatrix(num_classes=len(class_dict))\n",
    "\n",
    "for x, y in test_dataloader:\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pred = lightning_model(x)\n",
    "    cmat(pred, y)\n",
    "\n",
    "cmat_tensor = cmat.compute()\n",
    "cmat = cmat_tensor.numpy()\n",
    "\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=cmat,\n",
    "    class_names=class_dict.values(),\n",
    "    norm_colormap=matplotlib.colors.LogNorm()  \n",
    "    # normed colormaps highlight the off-diagonals \n",
    "    # for high-accuracy models better\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e7426e8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \n",
      "[GCC 9.4.0]\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "pandas           : 1.4.1\n",
      "re               : 2.2.1\n",
      "pytorch_lightning: 1.5.1\n",
      "torch            : 1.10.1\n",
      "torchvision      : 0.11.2\n",
      "numpy            : 1.22.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"%watermark --iversions\";\n",
       "                var nbb_formatted_code = \"%watermark --iversions\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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    "%watermark --iversions"
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 ],
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